AI transcript
0:00:11 [MUSIC]
0:00:13 >> Hello, and welcome to the NVIDIA AI podcast.
0:00:15 I’m your host, Noah Craven.
0:00:17 There’s been a lot of talk over the past year or
0:00:22 two about whether or not AI will take jobs away from humans.
0:00:23 Our guest today, however,
0:00:27 is already using AI to connect more humans to more jobs,
0:00:30 which is good for job seekers and good for employers.
0:00:35 Companies in the United States alone spend $15 billion annually
0:00:38 on clicks to advertise their job vacancies,
0:00:43 but 95 percent of all job applications are abandoned before they’re completed.
0:00:47 That’s a big problem for a lot of job seekers and a lot of employers.
0:00:51 Mikhail Rajesh is the co-founder and CEO of Sonic Jobs,
0:00:55 a startup and a member of NVIDIA’s Inception Program for startups,
0:00:58 that’s making it easier for more candidates to apply to more jobs.
0:01:01 Sonic Jobs uses a unique approach to
0:01:03 agentic AI and web agents,
0:01:06 which I’m excited to dig into with Mikhail now.
0:01:08 So let’s welcome him on. Mikhail Rajesh,
0:01:12 welcome and thank you so much for joining the NVIDIA AI podcast.
0:01:13 >> Thanks, Noah, for having me.
0:01:15 >> So there is a lot in there,
0:01:17 in the intro teasing about Sonic Jobs.
0:01:19 But since we have you,
0:01:23 why don’t you tell the audience a bit about what Sonic Jobs is?
0:01:26 >> Yeah. So companies in the US spend
0:01:29 $15 billion on advertising their vacancies,
0:01:34 and the way they do that has moved from per listing,
0:01:37 which is how they used to advertise to per click,
0:01:39 where it’s performance-based.
0:01:45 The most important metric in that performance-based recruitment advertising space
0:01:49 is the conversion from the paid click to the completed apply.
0:01:51 As you touched on in your introduction,
0:01:53 industry-wide today,
0:01:57 only 5 percent of people complete the job application,
0:02:01 which means 95 percent of them abandoned that application.
0:02:02 >> That’s astounding.
0:02:05 Only 5 percent of applications that get
0:02:08 started are actually completed and submitted.
0:02:09 I figured, yeah,
0:02:12 I know applications get abandoned, but that is amazing.
0:02:15 Forgive me, I just wanted to express that.
0:02:19 >> Yeah, and the biggest reason for that is the redirection.
0:02:22 So candidates today still go through
0:02:27 the same 1.0 experience as you did in the 90s,
0:02:29 where when you want to apply for the job,
0:02:30 you start on the job platform,
0:02:33 and then for each job that you want to apply for,
0:02:36 you get redirected to the company site to apply.
0:02:38 As soon as you’re redirected,
0:02:40 you have a 70 percent bounce rate.
0:02:45 So the majority, the biggest factor in that 95 percent is this redirection step,
0:02:50 which causes the huge friction that we experience in the market.
0:02:53 >> Right. So just so I understand from the job seekers perspective,
0:02:55 I’m on whatever, I don’t even know,
0:02:59 dice or LinkedIn or Monster indeed,
0:03:00 whatever they are, the job boards.
0:03:04 I see a job for podcaster at NVIDIA and I click,
0:03:06 I’m making this all the way up, I don’t know, but I click,
0:03:08 and so then the redirect is,
0:03:11 it takes me from the job board to, in this case, NVIDIA,
0:03:13 but wherever the employer is site,
0:03:18 and you said you’re losing 70 percent of people just on the bounce.
0:03:19 >> Exactly right. Yes.
0:03:20 >> Okay.
0:03:22 >> Historically, people have tried to build
0:03:26 APIs to connect this whole ecosystem,
0:03:29 but it’s too fragmented an ecosystem.
0:03:31 So there’s hundreds of job platforms,
0:03:33 some of which you touched on just now.
0:03:39 There’s over 200,000 employers that advertise vacancies in America,
0:03:41 and there’s over 10 million jobs,
0:03:43 each of which will have different flows and
0:03:47 different requirements for each application process,
0:03:50 and so APIs haven’t worked,
0:03:53 which is why you still have this experience that we touched on,
0:03:56 which is actually still the 1.0 experience,
0:04:00 web 1.0 experience where you start and then you’re redirected,
0:04:01 and you have to complete the application.
0:04:04 >> So the idea of the API would be,
0:04:08 if I was on a job board where I’d already entered some info,
0:04:10 or maybe they have a profile of me,
0:04:11 and it’s got my basic info,
0:04:16 and the API theoretically would take that info,
0:04:20 and as I’m redirected to the employer site,
0:04:21 it would carry some of that with me,
0:04:24 so I don’t have to re-enter all the basic stuff
0:04:26 that you always have to re-enter, got it.
0:04:28 But those APIs don’t work.
0:04:30 I’m seeing you nod, I’m thinking,
0:04:32 well, the whole web is made out of spaghetti at this point,
0:04:33 but I’m not a developer.
0:04:35 So is that what’s going on with the APIs?
0:04:38 >> Yeah, exactly. It’s not worked,
0:04:40 and just to elaborate on your point,
0:04:44 ideally, you would apply directly on the platform that you’re on.
0:04:47 When you’re on booking.com,
0:04:51 you don’t get redirected to Hilton if you want to book a room,
0:04:53 you can just book on booking.com.
0:04:56 So if you take that analogy here,
0:04:59 you should be able to apply directly on the job platform,
0:05:01 which is exactly what we’ve built.
0:05:03 So instead of using APIs,
0:05:05 what we use is AI agents,
0:05:07 which I know you want to talk about,
0:05:09 I’m happy to go deeper into that,
0:05:13 where effectively we understand using
0:05:15 computer vision and also the HTML,
0:05:19 every single input field of the job application,
0:05:23 so that when the candidates on the job site impresses apply,
0:05:27 we ask all of the relevant questions directly on the site,
0:05:30 and then the AI agent takes all of that data,
0:05:32 and on behalf of the candidate,
0:05:36 submits their data on to the job application flow
0:05:38 to complete the application.
0:05:39 And so instead of that 5%,
0:05:41 which we touched on earlier,
0:05:43 and that 95% wastage,
0:05:45 we’ve got a 26% conversion,
0:05:50 so five times better than a standard job application flow,
0:05:52 which is fantastic.
0:05:55 It is, five-fold increase.
0:05:57 I want to divert from my notes here
0:05:59 and ask you about that business end of things,
0:06:01 because I’ll just ask you this question,
0:06:03 then we’ll come back to the business stuff.
0:06:05 But how long did it take you to achieve
0:06:08 that five-fold increase in performance
0:06:10 over the industry standard?
0:06:13 Yeah, we’ve been building our technology for five years,
0:06:19 and it combines what we call traditional AI with LLMs
0:06:21 and kind of the new wave of AIs.
0:06:23 Yeah, happy to talk more about that as well.
0:06:25 Yeah, yeah, so pin in that bad question for me.
0:06:26 So that’s a teaser for later.
0:06:27 Stick around, audience.
0:06:32 We’ll find out about how Sonic Johns has grown so quickly.
0:06:33 But you mentioned computer vision,
0:06:36 and you mentioned looking at the HTML.
0:06:38 So maybe let’s dig in.
0:06:40 I sort of want to ask you from two perspectives.
0:06:41 From the user perspective,
0:06:44 I just want to clarify kind of the sequence
0:06:47 and where the agent comes in and starts working.
0:06:51 And then I’m curious about how the computer vision works,
0:06:52 and then also what’s happening
0:06:54 at the use of HTML behind the scenes.
0:06:57 Yeah, so from the user perspective,
0:06:58 they stay on site.
0:07:00 So you get this win-win scenario
0:07:03 where the candidates answering all the questions,
0:07:05 they stay on the site that they’re on.
0:07:08 You know, the good analogy is the booking.com experience.
0:07:12 You fill out all of the details on the platform itself.
0:07:14 On the ground board to–
0:07:16 Yeah, exactly, exactly.
0:07:16 And then you’re done.
0:07:18 You press supply, it’s all done.
0:07:21 From the employer’s perspective
0:07:24 and kind of how that all maps through,
0:07:27 because we’re using AI agents
0:07:31 and the input fields are all publicly facing
0:07:34 from a job application flow perspective,
0:07:37 they don’t need to do any development work.
0:07:40 So we’re asking exactly the questions that are needed
0:07:43 for that job apply flow that’s on their website,
0:07:46 and they don’t need to kind of build an API
0:07:48 or have tech resources, et cetera.
0:07:50 And they only pay when they–
0:07:52 The employer, et cetera, exactly.
0:07:55 And they only pay when they receive the application.
0:07:56 So they pay–
0:07:57 Right, right, like that conversion–
0:07:58 At the app–
0:07:59 At the Alphabet, smile, yeah.
0:08:00 Right.
0:08:05 So is this CV, is your agent sort of looking
0:08:08 at the job application web page
0:08:12 and visually making sense of what’s being asked?
0:08:13 Like, oh, this is the first name form,
0:08:15 this is the last name form, this is the,
0:08:20 why do you want to work at, you know, Acme Company SA field?
0:08:22 Like, is that what the CV is doing?
0:08:23 You said CV, I think–
0:08:25 The computer vision element.
0:08:27 It jumped out to me when you said computer vision before.
0:08:27 So–
0:08:30 Yeah, CV in the UK means–
0:08:31 Oh, right, curriculum.
0:08:32 Sorry, so I got–
0:08:34 Of course, my bad.
0:08:37 So, yeah, that’s exactly what the agent’s doing.
0:08:41 The average job application form has six pages
0:08:43 and 40 input fields.
0:08:45 And so the agent is understanding
0:08:46 every single input field.
0:08:49 It can be radio buttons, drop downs, open questions,
0:08:51 open text, conditional questions,
0:08:54 all that sort of stuff is transforming that
0:08:57 to kind of a context and to a structure
0:08:59 and then asking those questions exactly right.
0:09:02 And also inputting that data back into those questions.
0:09:05 So maybe that can lead us into this talk
0:09:07 about AI agents sort of broadly,
0:09:09 because I think a lot of what I’m thinking about
0:09:13 goes back to like a year, even a year and a half ago
0:09:15 where there was buzz around auto GPT
0:09:17 and some other open source projects.
0:09:20 Talk to us about agents, how Sonic Jobs uses them,
0:09:23 and maybe how that’s different from other approaches.
0:09:26 Yeah, so you’re exactly right now.
0:09:30 12 months ago, auto GPT, baby AGI.
0:09:31 Baby AGI, yeah, yeah.
0:09:32 Captured our imagination.
0:09:36 It was like the agent that can do everything.
0:09:41 What we’ve seen is that the architecture of the agent
0:09:45 needs to combine very tightly the application layer
0:09:47 and the reasoning layer.
0:09:51 And that’s because accuracy and reliability
0:09:53 are really important for companies.
0:09:59 Like the challenge with baby AGI and auto GPT
0:10:02 was that LLMs are not deterministic.
0:10:05 And so they can’t do step one, step two, step three,
0:10:08 step four in the order that you want them to
0:10:12 whilst an enterprise client wants the output
0:10:15 and the workflow to be structured in a particular manner,
0:10:17 which is domain specific.
0:10:19 You know, they’ll have specific steps to,
0:10:22 and again, our domain is obviously job applications.
0:10:27 And that means that it’s really important to combine
0:10:29 in our experience, in our view,
0:10:32 to combine what we think of as traditional AI,
0:10:35 which is very high on accuracy
0:10:39 and lower on, let’s say, generalizability.
0:10:41 Combining that with the LLMs,
0:10:44 which can actually generate data
0:10:48 and use the memory of the successful training data
0:10:52 to basically use that for future,
0:10:54 use cases for future workflows, et cetera.
0:10:56 And so you’ve kind of got this combination,
0:10:59 which we think is really powerful
0:11:02 and very domain specific.
0:11:04 And that’s where our experience
0:11:08 of kind of this vertical AI agent is that,
0:11:13 it’s much more powerful for the specific B2B use cases
0:11:17 versus this general agent that can kind of do everything,
0:11:20 which actually ends up doing nothing
0:11:22 rather than being able to do everything.
0:11:24 So it’s kind of interesting.
0:11:26 – This is me we’re talking about, I’m not a developer,
0:11:28 but my own experiments with those early systems
0:11:31 did a whole lot of nothing as it turned out,
0:11:32 but that’s a separate take.
0:11:35 So this approach to agents that you’re talking about,
0:11:37 when Sonic Jobs, well, at whatever point
0:11:39 in the Sonic Jobs story,
0:11:44 did you sort of see this approach to agentic AI
0:11:49 and kind of think, oh, this maybe is a good solution
0:11:51 to this problem we’re seeing with the redirects
0:11:55 and the other things in the recruiting industry,
0:11:57 or did it come about another way?
0:11:59 What was kind of the moment where you thought,
0:12:01 oh, this could be the approach we use?
0:12:03 – Yeah, it’s a really good question.
0:12:07 I spent three years before Sonic Jobs at AutoTrader,
0:12:10 which is a karma place.
0:12:15 And I became obsessed with this idea
0:12:18 that you could remove friction
0:12:23 and have much better engagement by users.
0:12:27 And particularly as mobile came in,
0:12:31 what I saw in the job space was that
0:12:33 this redirection point of friction
0:12:37 was becoming a bigger and bigger issue.
0:12:40 And we thought about it in the context
0:12:44 of really creating this API-less API.
0:12:48 And so we create kind of this version of an API
0:12:51 where actually the company did no tech work,
0:12:55 but the candidate could have a really seamless experience
0:12:58 like they do on booking.com.
0:13:02 And yeah, it’s taken us the majority of our existence
0:13:03 to build what we’ve built.
0:13:05 So to answer your question,
0:13:09 we’re 28 people, 24 engineers,
0:13:13 four commercial people including myself.
0:13:16 And over the five years, we’ve spent over three years
0:13:18 just building out the technology.
0:13:22 And last year, we launched in the U.S.
0:13:24 That’s gone super well.
0:13:28 And we’re primarily a U.S. business now.
0:13:30 But yeah, the engineering part of,
0:13:32 and the agent part of our business
0:13:34 is at the very heart of what we do.
0:13:37 – Of course, we were talking before we started recording
0:13:41 that you moved from England to the U.S.
0:13:44 Is the rest of the company global in the U.S. and England
0:13:47 where are the semi-jobs people?
0:13:49 – Yeah, we’re a fully remote team.
0:13:51 I should mention also my co-founder
0:13:54 has a background in robotics and AI.
0:13:57 So that’s how we all kind of came to this conclusion.
0:14:00 And at the time we started, this didn’t even have a name.
0:14:03 It’s amazing that everything’s called AI agents now.
0:14:08 It’s a term and agents kind of represent lots of things now.
0:14:11 Maybe we could call what we do AI web agents,
0:14:13 specifically for the web.
0:14:17 And we used to call it kind of AI plus RPA.
0:14:19 No one had a clue what that meant.
0:14:21 – What’s RPA?
0:14:22 – Robotic process automation.
0:14:26 So it’s kind of workflow automation,
0:14:28 which is a little bit more manual.
0:14:30 And so we were kind of combining the two.
0:14:32 But yeah, the agent term has taken off
0:14:34 and it’s come a long way in the last five years.
0:14:36 – I want to ask you more about the agent thing.
0:14:39 I want to go back for a second, though, to the company.
0:14:42 You mentioned that the business has sort of taken off
0:14:44 and shifted focus to the U.S.
0:14:47 So you’re primarily working with U.S.-based employers?
0:14:49 – Yeah, we work with the largest,
0:14:51 some of the largest U.S. employers today.
0:14:54 So Dish, CBS, Walgreens.
0:14:59 And we’ve seen huge traction for our use case.
0:15:02 – And then also mentioned the intro
0:15:05 that you’re in the NVIDIA deception program.
0:15:07 – Yep, NVIDIA has been awesome with us.
0:15:09 And we’re excited.
0:15:13 We’re building kind of deeper RAG infrastructure
0:15:15 or with NVIDIA’s help.
0:15:18 And it’s been, yeah, great collaboration.
0:15:21 – What’s on the Sonic Jobs roadmap right now?
0:15:23 Are you, is it just businesses boom in
0:15:24 and you’re doing your thing?
0:15:28 Is there a product roadmap that you’re oriented towards?
0:15:30 – Yeah, I think it’s really important to emphasize
0:15:34 how early we are in this journey on agents in particular.
0:15:36 And it’s taken us five years,
0:15:40 but we’re still, I think as the whole ecosystem,
0:15:42 still at the very start of that journey.
0:15:46 And so today our agent can interact
0:15:50 with about 60,000 jobs in the U.S.
0:15:54 So it understands the input fields from 60,000 jobs.
0:15:57 The total market size just in the U.S. alone
0:15:59 is 10 million jobs.
0:16:02 And so there’s a long way to go in terms of,
0:16:05 even with a specific domain,
0:16:07 there’s a long way to go in terms of being able
0:16:11 to understand every single input field, every single job.
0:16:15 – Is it a matter of just continuing to chip away
0:16:18 at a sort of very big unwieldly problem,
0:16:20 which is what you described earlier
0:16:24 about making sense of the multi-page application form
0:16:26 or are there other technical things?
0:16:28 We’re talking agents on the pod.
0:16:31 So if there’s more interesting stuff you can talk about,
0:16:32 I would love to hear.
0:16:34 – Yeah, no, I love this, I love this.
0:16:37 The real answer is we don’t fully know.
0:16:41 There is a scale element to this
0:16:46 where the more that you get successful applications
0:16:49 going through, and we’ve got now millions
0:16:52 of successful applications that have gone through our agent,
0:16:57 the more the model learns and is able to adapt
0:17:00 to the next job application flow that it sees,
0:17:03 which has been very successful as a technique
0:17:06 and continues to bear fruit for us,
0:17:09 the more clients we work with, et cetera.
0:17:13 There’s a reasoning, a general reasoning layer.
0:17:16 So where you think about the kind of LLMs
0:17:18 are not great as we talked about earlier,
0:17:21 following steps, planning, et cetera.
0:17:23 I’m confident that will get better as well,
0:17:27 and that will enhance our kind of vertical approach
0:17:30 within the application layer as well.
0:17:32 I think it’ll be a combination of a few things
0:17:35 that basically end up kind of being able
0:17:40 to create a vertical AI agent across the entire domain.
0:17:43 – Is it, you’re talking about the application layer
0:17:44 and the reasoning layer.
0:17:47 And if I understand it, or to the extent I understand it,
0:17:49 it makes a lot of sense to me that you know,
0:17:53 you’ve got whether it’s processing job applications
0:17:58 or submitting, I should say, I guess,
0:18:01 or another business enterprise task,
0:18:02 whatever it might be.
0:18:05 So I understand that sort of the verticalized,
0:18:08 specific tasks that the agent is being developed to do.
0:18:09 And then underneath that,
0:18:12 you’ve got this reasoning layer you talked about,
0:18:14 which is the LLM.
0:18:14 – Yep.
0:18:17 – And so when you’re talking about the reasoning layer
0:18:20 needing to get better, correct me if I’m wrong,
0:18:23 but my understanding is you’re not building foundational models
0:18:27 from scratch, but you are doing a lot of work
0:18:30 to kind of fine-tuning them the right way to say it.
0:18:31 – Exactly right.
0:18:34 – To get them to perform how you want them to
0:18:36 in conjunction with your system.
0:18:38 So all this is getting to my question,
0:18:42 which is, is there a lot that a company like yours can do
0:18:45 to kind of tweak the reasoning layer?
0:18:49 Or is it mainly a matter of waiting for the,
0:18:51 the various companies of the world
0:18:53 who are releasing foundational models,
0:18:56 is it kind of more like waiting for them
0:19:00 to ship something better and then you can,
0:19:02 or is it kind of a combo of both?
0:19:06 – No, there’s a ton you can do on the fine-tuning side.
0:19:09 And also what we call on the tooling side.
0:19:13 So traditional AI now is referred to almost as tools
0:19:17 and the LLMs can combine with these tools
0:19:20 and the fine-tuning that you’ve put on top of the LLMs
0:19:23 to create this kind of hybrid structure.
0:19:25 And you touched on it and then it’s an important point
0:19:30 to emphasize in domain specific workflows,
0:19:34 you don’t want randomized outputs.
0:19:38 You want predictable, accurate outputs.
0:19:42 And so depending on your domain,
0:19:46 you need to structure the architecture
0:19:50 so that you might even choose to kind of hard code
0:19:55 or create specific layers that do specific tasks
0:19:57 and then specific layers that create,
0:20:00 that do reasoning tasks or do error resolution tasks
0:20:03 or do input detection tasks.
0:20:07 And so our view, and you asked about kind of all developers,
0:20:08 so I’ll maybe touch on that.
0:20:13 Our view is that there are always going to be
0:20:18 application layer architecture that’s going to be needed
0:20:22 to create a vertical specific use case
0:20:24 for a particular enterprise.
0:20:27 And that’s going to be always really valuable.
0:20:29 And again, you’re kind of riding on the wave
0:20:32 of the LLMs becoming smarter because then you need to do,
0:20:35 your value add can be more and more
0:20:37 and you can scale faster and faster.
0:20:39 But there’s a ton you can do,
0:20:42 even as a small company like ourselves,
0:20:45 to create that domain specificity,
0:20:47 which is hugely valuable.
0:20:51 – So your technology, and not that Sonic Jobs
0:20:56 is focused on anything besides the talent acquisition industry,
0:20:59 but is the approach you’re taking,
0:21:01 could it serve as a framework,
0:21:06 even just conceptually for kind of similar work
0:21:07 in a different domain?
0:21:10 – Yeah, I would answer your question in two ways.
0:21:14 One is we think that as you get better
0:21:16 at a specific domain and vertical,
0:21:18 it actually gives you the springboard
0:21:21 to potentially explore other domains,
0:21:23 which we think is interesting in a way
0:21:27 that we think is more valuable and more likely
0:21:30 to be more successful than starting as a generalist
0:21:32 and moving to being a generalist,
0:21:33 actually starting at a vertical
0:21:35 and then moving out to other virtuals
0:21:38 we think could be more fruitful.
0:21:41 The second thing is, and it’s a little bit of a,
0:21:43 as I mentioned, I’m new to Silicon Valley,
0:21:48 so I’ll say how I see it, obviously, as someone new.
0:21:52 I think AI and AI agents today
0:21:54 have been largely built by people
0:21:57 who are excited about the technology
0:21:59 and looking for a problem.
0:22:02 I think there’s a lot of room,
0:22:05 and I think the other group is the group of people
0:22:08 that have a problem, whether that’s a B2C problem
0:22:12 or a B2B problem, and then look at AI
0:22:15 as creating a solution that could never be created before.
0:22:19 I think the way we’ve done it is the latter
0:22:22 in that we had a problem and we looked at AI
0:22:23 to create a unique use case,
0:22:26 and I think I would encourage anyone listening to this,
0:22:29 and you mentioned that there’s a lot of engineers
0:22:31 that listen to this.
0:22:34 I would encourage anyone with a problem
0:22:38 to know that it’s open and not too often.
0:22:40 It can feel like a wall of garden AI
0:22:42 coming in from the outside,
0:22:44 particularly if you’re not from Silicon Valley.
0:22:47 I would encourage people to lean into the problems
0:22:48 that they have.
0:22:52 – I mean, even at my sort of 50,000-foot level,
0:22:54 what you said just kind of rang true.
0:22:58 Simple as it sounds, that idea of LLMs in particular,
0:23:01 almost by nature, are this semi-black box
0:23:03 or just this emergent capabilities,
0:23:05 all these words that are used to describe
0:23:09 this kind of general process of figuring out what it can do,
0:23:11 and so that approach of starting with,
0:23:13 I don’t know, I personally, if you give me a blank sheet
0:23:15 of paper, I have the hardest time coming up with words,
0:23:18 but if you say, “Hey, here’s what we need,”
0:23:20 so yeah, there’s kind of a similar thing
0:23:22 resonated with me hearing you say that.
0:23:25 All right, got one last question for you before we wrap up.
0:23:27 It’s a little bit of a flip.
0:23:30 We usually like to ask how the work you’re doing
0:23:33 and AI’s impact on the work you’re doing
0:23:35 is gonna affect things going forward,
0:23:37 which we’ve talked about a little bit already,
0:23:42 but since you’re in the hiring space, so to speak,
0:23:44 and we’ve been talking about the inefficiency
0:23:47 of the posting model, changing the conversions
0:23:49 and technology like Sonic Jobs,
0:23:50 increasing that conversion rate.
0:23:53 Again, to put you on a spot here,
0:23:55 what advice would you give to a company
0:23:58 who’s trying to hire right now,
0:24:01 depending on the industry, we’re hearing a lot about
0:24:03 the economy and employment in certain industries,
0:24:07 talent crunches and others, can’t find a qualified person.
0:24:09 So kind of given that broad spectrum,
0:24:12 are there any kind of high level pieces of advice
0:24:14 you’d give to somebody out there
0:24:16 who’s trying to advertise and hire for roles,
0:24:17 but just having a hard time?
0:24:21 – Yeah, I think the simplest piece of advice would be
0:24:26 to go to a job platform, find your job,
0:24:29 and apply for your job on the job platform.
0:24:32 What most companies do is apply for their job
0:24:37 or look at their apply flow on their own company site.
0:24:38 – Oh, but not from the, yeah, okay.
0:24:41 – But you’ve got to remember that most candidates
0:24:43 start their journey on a job platform,
0:24:47 whether that’s LinkedIn or Indeed or Sonic Jobs,
0:24:49 and really think about that experience
0:24:51 from the job seekers perspective,
0:24:55 because if you do that, you’ll optimize your own conversion
0:24:59 and have a better experience for your candidates,
0:25:00 which is pretty cool.
0:25:02 – For folks who are listening
0:25:04 and would like to learn more about Sonic Jobs
0:25:08 from whatever perspective, the technical perspective,
0:25:12 maybe they’re an employer who’s looking for new ways to hire,
0:25:13 or I guess maybe the job seekers
0:25:15 can go look at job listings as well.
0:25:18 Where would you send people to go online?
0:25:20 What all is on the Sonic Jobs website?
0:25:23 And then is there social media as well
0:25:24 that listeners can follow?
0:25:27 – Yeah, so if you’re an employer
0:25:30 and you want to talk more, add me on LinkedIn,
0:25:35 Michael Raja, if you’re a job seeker, AI engineer
0:25:36 that think we’re doing something cool,
0:25:39 we’re hiring, so please reach out to me.
0:25:41 And if you just want to learn about our technology,
0:25:46 we’ve created a page, Sonic Jobs/AI agent on our website
0:25:49 where you can learn more about our technology.
0:25:51 – Great, sidejobs.com.
0:25:51 – Exactly.
0:25:54 – We’re into the age that used to be
0:25:56 way back when it was dot com,
0:25:57 but people would use other ones
0:25:58 if they couldn’t get the name.
0:26:00 And then it was all dot com, org.
0:26:03 Now we’re getting like the dot AI and the other search.
0:26:04 I don’t remember to ask.
0:26:07 So sonicjobs.com, fantastic.
0:26:09 McKeil, I feel like we could talk Asians
0:26:12 and talk the future of the web for longer,
0:26:15 let alone the talent acquisition industry.
0:26:17 But I think that this gives a great purview
0:26:20 into what Sonic Jobs is doing, has been doing,
0:26:24 and also kind of bringing back up the topic of agents,
0:26:25 which is interesting to think about
0:26:28 how things have changed in a short amount of time.
0:26:29 But it feels like a long amount of time
0:26:31 since Jena AI hit the scene.
0:26:34 All the best to you and your whole team.
0:26:35 Thank you for coming on.
0:26:38 And yeah, any words in closing?
0:26:40 – No, thanks very much for having me, really exciting.
0:26:41 – Fantastic.
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0:00:13 >> Hello, and welcome to the NVIDIA AI podcast.
0:00:15 I’m your host, Noah Craven.
0:00:17 There’s been a lot of talk over the past year or
0:00:22 two about whether or not AI will take jobs away from humans.
0:00:23 Our guest today, however,
0:00:27 is already using AI to connect more humans to more jobs,
0:00:30 which is good for job seekers and good for employers.
0:00:35 Companies in the United States alone spend $15 billion annually
0:00:38 on clicks to advertise their job vacancies,
0:00:43 but 95 percent of all job applications are abandoned before they’re completed.
0:00:47 That’s a big problem for a lot of job seekers and a lot of employers.
0:00:51 Mikhail Rajesh is the co-founder and CEO of Sonic Jobs,
0:00:55 a startup and a member of NVIDIA’s Inception Program for startups,
0:00:58 that’s making it easier for more candidates to apply to more jobs.
0:01:01 Sonic Jobs uses a unique approach to
0:01:03 agentic AI and web agents,
0:01:06 which I’m excited to dig into with Mikhail now.
0:01:08 So let’s welcome him on. Mikhail Rajesh,
0:01:12 welcome and thank you so much for joining the NVIDIA AI podcast.
0:01:13 >> Thanks, Noah, for having me.
0:01:15 >> So there is a lot in there,
0:01:17 in the intro teasing about Sonic Jobs.
0:01:19 But since we have you,
0:01:23 why don’t you tell the audience a bit about what Sonic Jobs is?
0:01:26 >> Yeah. So companies in the US spend
0:01:29 $15 billion on advertising their vacancies,
0:01:34 and the way they do that has moved from per listing,
0:01:37 which is how they used to advertise to per click,
0:01:39 where it’s performance-based.
0:01:45 The most important metric in that performance-based recruitment advertising space
0:01:49 is the conversion from the paid click to the completed apply.
0:01:51 As you touched on in your introduction,
0:01:53 industry-wide today,
0:01:57 only 5 percent of people complete the job application,
0:02:01 which means 95 percent of them abandoned that application.
0:02:02 >> That’s astounding.
0:02:05 Only 5 percent of applications that get
0:02:08 started are actually completed and submitted.
0:02:09 I figured, yeah,
0:02:12 I know applications get abandoned, but that is amazing.
0:02:15 Forgive me, I just wanted to express that.
0:02:19 >> Yeah, and the biggest reason for that is the redirection.
0:02:22 So candidates today still go through
0:02:27 the same 1.0 experience as you did in the 90s,
0:02:29 where when you want to apply for the job,
0:02:30 you start on the job platform,
0:02:33 and then for each job that you want to apply for,
0:02:36 you get redirected to the company site to apply.
0:02:38 As soon as you’re redirected,
0:02:40 you have a 70 percent bounce rate.
0:02:45 So the majority, the biggest factor in that 95 percent is this redirection step,
0:02:50 which causes the huge friction that we experience in the market.
0:02:53 >> Right. So just so I understand from the job seekers perspective,
0:02:55 I’m on whatever, I don’t even know,
0:02:59 dice or LinkedIn or Monster indeed,
0:03:00 whatever they are, the job boards.
0:03:04 I see a job for podcaster at NVIDIA and I click,
0:03:06 I’m making this all the way up, I don’t know, but I click,
0:03:08 and so then the redirect is,
0:03:11 it takes me from the job board to, in this case, NVIDIA,
0:03:13 but wherever the employer is site,
0:03:18 and you said you’re losing 70 percent of people just on the bounce.
0:03:19 >> Exactly right. Yes.
0:03:20 >> Okay.
0:03:22 >> Historically, people have tried to build
0:03:26 APIs to connect this whole ecosystem,
0:03:29 but it’s too fragmented an ecosystem.
0:03:31 So there’s hundreds of job platforms,
0:03:33 some of which you touched on just now.
0:03:39 There’s over 200,000 employers that advertise vacancies in America,
0:03:41 and there’s over 10 million jobs,
0:03:43 each of which will have different flows and
0:03:47 different requirements for each application process,
0:03:50 and so APIs haven’t worked,
0:03:53 which is why you still have this experience that we touched on,
0:03:56 which is actually still the 1.0 experience,
0:04:00 web 1.0 experience where you start and then you’re redirected,
0:04:01 and you have to complete the application.
0:04:04 >> So the idea of the API would be,
0:04:08 if I was on a job board where I’d already entered some info,
0:04:10 or maybe they have a profile of me,
0:04:11 and it’s got my basic info,
0:04:16 and the API theoretically would take that info,
0:04:20 and as I’m redirected to the employer site,
0:04:21 it would carry some of that with me,
0:04:24 so I don’t have to re-enter all the basic stuff
0:04:26 that you always have to re-enter, got it.
0:04:28 But those APIs don’t work.
0:04:30 I’m seeing you nod, I’m thinking,
0:04:32 well, the whole web is made out of spaghetti at this point,
0:04:33 but I’m not a developer.
0:04:35 So is that what’s going on with the APIs?
0:04:38 >> Yeah, exactly. It’s not worked,
0:04:40 and just to elaborate on your point,
0:04:44 ideally, you would apply directly on the platform that you’re on.
0:04:47 When you’re on booking.com,
0:04:51 you don’t get redirected to Hilton if you want to book a room,
0:04:53 you can just book on booking.com.
0:04:56 So if you take that analogy here,
0:04:59 you should be able to apply directly on the job platform,
0:05:01 which is exactly what we’ve built.
0:05:03 So instead of using APIs,
0:05:05 what we use is AI agents,
0:05:07 which I know you want to talk about,
0:05:09 I’m happy to go deeper into that,
0:05:13 where effectively we understand using
0:05:15 computer vision and also the HTML,
0:05:19 every single input field of the job application,
0:05:23 so that when the candidates on the job site impresses apply,
0:05:27 we ask all of the relevant questions directly on the site,
0:05:30 and then the AI agent takes all of that data,
0:05:32 and on behalf of the candidate,
0:05:36 submits their data on to the job application flow
0:05:38 to complete the application.
0:05:39 And so instead of that 5%,
0:05:41 which we touched on earlier,
0:05:43 and that 95% wastage,
0:05:45 we’ve got a 26% conversion,
0:05:50 so five times better than a standard job application flow,
0:05:52 which is fantastic.
0:05:55 It is, five-fold increase.
0:05:57 I want to divert from my notes here
0:05:59 and ask you about that business end of things,
0:06:01 because I’ll just ask you this question,
0:06:03 then we’ll come back to the business stuff.
0:06:05 But how long did it take you to achieve
0:06:08 that five-fold increase in performance
0:06:10 over the industry standard?
0:06:13 Yeah, we’ve been building our technology for five years,
0:06:19 and it combines what we call traditional AI with LLMs
0:06:21 and kind of the new wave of AIs.
0:06:23 Yeah, happy to talk more about that as well.
0:06:25 Yeah, yeah, so pin in that bad question for me.
0:06:26 So that’s a teaser for later.
0:06:27 Stick around, audience.
0:06:32 We’ll find out about how Sonic Johns has grown so quickly.
0:06:33 But you mentioned computer vision,
0:06:36 and you mentioned looking at the HTML.
0:06:38 So maybe let’s dig in.
0:06:40 I sort of want to ask you from two perspectives.
0:06:41 From the user perspective,
0:06:44 I just want to clarify kind of the sequence
0:06:47 and where the agent comes in and starts working.
0:06:51 And then I’m curious about how the computer vision works,
0:06:52 and then also what’s happening
0:06:54 at the use of HTML behind the scenes.
0:06:57 Yeah, so from the user perspective,
0:06:58 they stay on site.
0:07:00 So you get this win-win scenario
0:07:03 where the candidates answering all the questions,
0:07:05 they stay on the site that they’re on.
0:07:08 You know, the good analogy is the booking.com experience.
0:07:12 You fill out all of the details on the platform itself.
0:07:14 On the ground board to–
0:07:16 Yeah, exactly, exactly.
0:07:16 And then you’re done.
0:07:18 You press supply, it’s all done.
0:07:21 From the employer’s perspective
0:07:24 and kind of how that all maps through,
0:07:27 because we’re using AI agents
0:07:31 and the input fields are all publicly facing
0:07:34 from a job application flow perspective,
0:07:37 they don’t need to do any development work.
0:07:40 So we’re asking exactly the questions that are needed
0:07:43 for that job apply flow that’s on their website,
0:07:46 and they don’t need to kind of build an API
0:07:48 or have tech resources, et cetera.
0:07:50 And they only pay when they–
0:07:52 The employer, et cetera, exactly.
0:07:55 And they only pay when they receive the application.
0:07:56 So they pay–
0:07:57 Right, right, like that conversion–
0:07:58 At the app–
0:07:59 At the Alphabet, smile, yeah.
0:08:00 Right.
0:08:05 So is this CV, is your agent sort of looking
0:08:08 at the job application web page
0:08:12 and visually making sense of what’s being asked?
0:08:13 Like, oh, this is the first name form,
0:08:15 this is the last name form, this is the,
0:08:20 why do you want to work at, you know, Acme Company SA field?
0:08:22 Like, is that what the CV is doing?
0:08:23 You said CV, I think–
0:08:25 The computer vision element.
0:08:27 It jumped out to me when you said computer vision before.
0:08:27 So–
0:08:30 Yeah, CV in the UK means–
0:08:31 Oh, right, curriculum.
0:08:32 Sorry, so I got–
0:08:34 Of course, my bad.
0:08:37 So, yeah, that’s exactly what the agent’s doing.
0:08:41 The average job application form has six pages
0:08:43 and 40 input fields.
0:08:45 And so the agent is understanding
0:08:46 every single input field.
0:08:49 It can be radio buttons, drop downs, open questions,
0:08:51 open text, conditional questions,
0:08:54 all that sort of stuff is transforming that
0:08:57 to kind of a context and to a structure
0:08:59 and then asking those questions exactly right.
0:09:02 And also inputting that data back into those questions.
0:09:05 So maybe that can lead us into this talk
0:09:07 about AI agents sort of broadly,
0:09:09 because I think a lot of what I’m thinking about
0:09:13 goes back to like a year, even a year and a half ago
0:09:15 where there was buzz around auto GPT
0:09:17 and some other open source projects.
0:09:20 Talk to us about agents, how Sonic Jobs uses them,
0:09:23 and maybe how that’s different from other approaches.
0:09:26 Yeah, so you’re exactly right now.
0:09:30 12 months ago, auto GPT, baby AGI.
0:09:31 Baby AGI, yeah, yeah.
0:09:32 Captured our imagination.
0:09:36 It was like the agent that can do everything.
0:09:41 What we’ve seen is that the architecture of the agent
0:09:45 needs to combine very tightly the application layer
0:09:47 and the reasoning layer.
0:09:51 And that’s because accuracy and reliability
0:09:53 are really important for companies.
0:09:59 Like the challenge with baby AGI and auto GPT
0:10:02 was that LLMs are not deterministic.
0:10:05 And so they can’t do step one, step two, step three,
0:10:08 step four in the order that you want them to
0:10:12 whilst an enterprise client wants the output
0:10:15 and the workflow to be structured in a particular manner,
0:10:17 which is domain specific.
0:10:19 You know, they’ll have specific steps to,
0:10:22 and again, our domain is obviously job applications.
0:10:27 And that means that it’s really important to combine
0:10:29 in our experience, in our view,
0:10:32 to combine what we think of as traditional AI,
0:10:35 which is very high on accuracy
0:10:39 and lower on, let’s say, generalizability.
0:10:41 Combining that with the LLMs,
0:10:44 which can actually generate data
0:10:48 and use the memory of the successful training data
0:10:52 to basically use that for future,
0:10:54 use cases for future workflows, et cetera.
0:10:56 And so you’ve kind of got this combination,
0:10:59 which we think is really powerful
0:11:02 and very domain specific.
0:11:04 And that’s where our experience
0:11:08 of kind of this vertical AI agent is that,
0:11:13 it’s much more powerful for the specific B2B use cases
0:11:17 versus this general agent that can kind of do everything,
0:11:20 which actually ends up doing nothing
0:11:22 rather than being able to do everything.
0:11:24 So it’s kind of interesting.
0:11:26 – This is me we’re talking about, I’m not a developer,
0:11:28 but my own experiments with those early systems
0:11:31 did a whole lot of nothing as it turned out,
0:11:32 but that’s a separate take.
0:11:35 So this approach to agents that you’re talking about,
0:11:37 when Sonic Jobs, well, at whatever point
0:11:39 in the Sonic Jobs story,
0:11:44 did you sort of see this approach to agentic AI
0:11:49 and kind of think, oh, this maybe is a good solution
0:11:51 to this problem we’re seeing with the redirects
0:11:55 and the other things in the recruiting industry,
0:11:57 or did it come about another way?
0:11:59 What was kind of the moment where you thought,
0:12:01 oh, this could be the approach we use?
0:12:03 – Yeah, it’s a really good question.
0:12:07 I spent three years before Sonic Jobs at AutoTrader,
0:12:10 which is a karma place.
0:12:15 And I became obsessed with this idea
0:12:18 that you could remove friction
0:12:23 and have much better engagement by users.
0:12:27 And particularly as mobile came in,
0:12:31 what I saw in the job space was that
0:12:33 this redirection point of friction
0:12:37 was becoming a bigger and bigger issue.
0:12:40 And we thought about it in the context
0:12:44 of really creating this API-less API.
0:12:48 And so we create kind of this version of an API
0:12:51 where actually the company did no tech work,
0:12:55 but the candidate could have a really seamless experience
0:12:58 like they do on booking.com.
0:13:02 And yeah, it’s taken us the majority of our existence
0:13:03 to build what we’ve built.
0:13:05 So to answer your question,
0:13:09 we’re 28 people, 24 engineers,
0:13:13 four commercial people including myself.
0:13:16 And over the five years, we’ve spent over three years
0:13:18 just building out the technology.
0:13:22 And last year, we launched in the U.S.
0:13:24 That’s gone super well.
0:13:28 And we’re primarily a U.S. business now.
0:13:30 But yeah, the engineering part of,
0:13:32 and the agent part of our business
0:13:34 is at the very heart of what we do.
0:13:37 – Of course, we were talking before we started recording
0:13:41 that you moved from England to the U.S.
0:13:44 Is the rest of the company global in the U.S. and England
0:13:47 where are the semi-jobs people?
0:13:49 – Yeah, we’re a fully remote team.
0:13:51 I should mention also my co-founder
0:13:54 has a background in robotics and AI.
0:13:57 So that’s how we all kind of came to this conclusion.
0:14:00 And at the time we started, this didn’t even have a name.
0:14:03 It’s amazing that everything’s called AI agents now.
0:14:08 It’s a term and agents kind of represent lots of things now.
0:14:11 Maybe we could call what we do AI web agents,
0:14:13 specifically for the web.
0:14:17 And we used to call it kind of AI plus RPA.
0:14:19 No one had a clue what that meant.
0:14:21 – What’s RPA?
0:14:22 – Robotic process automation.
0:14:26 So it’s kind of workflow automation,
0:14:28 which is a little bit more manual.
0:14:30 And so we were kind of combining the two.
0:14:32 But yeah, the agent term has taken off
0:14:34 and it’s come a long way in the last five years.
0:14:36 – I want to ask you more about the agent thing.
0:14:39 I want to go back for a second, though, to the company.
0:14:42 You mentioned that the business has sort of taken off
0:14:44 and shifted focus to the U.S.
0:14:47 So you’re primarily working with U.S.-based employers?
0:14:49 – Yeah, we work with the largest,
0:14:51 some of the largest U.S. employers today.
0:14:54 So Dish, CBS, Walgreens.
0:14:59 And we’ve seen huge traction for our use case.
0:15:02 – And then also mentioned the intro
0:15:05 that you’re in the NVIDIA deception program.
0:15:07 – Yep, NVIDIA has been awesome with us.
0:15:09 And we’re excited.
0:15:13 We’re building kind of deeper RAG infrastructure
0:15:15 or with NVIDIA’s help.
0:15:18 And it’s been, yeah, great collaboration.
0:15:21 – What’s on the Sonic Jobs roadmap right now?
0:15:23 Are you, is it just businesses boom in
0:15:24 and you’re doing your thing?
0:15:28 Is there a product roadmap that you’re oriented towards?
0:15:30 – Yeah, I think it’s really important to emphasize
0:15:34 how early we are in this journey on agents in particular.
0:15:36 And it’s taken us five years,
0:15:40 but we’re still, I think as the whole ecosystem,
0:15:42 still at the very start of that journey.
0:15:46 And so today our agent can interact
0:15:50 with about 60,000 jobs in the U.S.
0:15:54 So it understands the input fields from 60,000 jobs.
0:15:57 The total market size just in the U.S. alone
0:15:59 is 10 million jobs.
0:16:02 And so there’s a long way to go in terms of,
0:16:05 even with a specific domain,
0:16:07 there’s a long way to go in terms of being able
0:16:11 to understand every single input field, every single job.
0:16:15 – Is it a matter of just continuing to chip away
0:16:18 at a sort of very big unwieldly problem,
0:16:20 which is what you described earlier
0:16:24 about making sense of the multi-page application form
0:16:26 or are there other technical things?
0:16:28 We’re talking agents on the pod.
0:16:31 So if there’s more interesting stuff you can talk about,
0:16:32 I would love to hear.
0:16:34 – Yeah, no, I love this, I love this.
0:16:37 The real answer is we don’t fully know.
0:16:41 There is a scale element to this
0:16:46 where the more that you get successful applications
0:16:49 going through, and we’ve got now millions
0:16:52 of successful applications that have gone through our agent,
0:16:57 the more the model learns and is able to adapt
0:17:00 to the next job application flow that it sees,
0:17:03 which has been very successful as a technique
0:17:06 and continues to bear fruit for us,
0:17:09 the more clients we work with, et cetera.
0:17:13 There’s a reasoning, a general reasoning layer.
0:17:16 So where you think about the kind of LLMs
0:17:18 are not great as we talked about earlier,
0:17:21 following steps, planning, et cetera.
0:17:23 I’m confident that will get better as well,
0:17:27 and that will enhance our kind of vertical approach
0:17:30 within the application layer as well.
0:17:32 I think it’ll be a combination of a few things
0:17:35 that basically end up kind of being able
0:17:40 to create a vertical AI agent across the entire domain.
0:17:43 – Is it, you’re talking about the application layer
0:17:44 and the reasoning layer.
0:17:47 And if I understand it, or to the extent I understand it,
0:17:49 it makes a lot of sense to me that you know,
0:17:53 you’ve got whether it’s processing job applications
0:17:58 or submitting, I should say, I guess,
0:18:01 or another business enterprise task,
0:18:02 whatever it might be.
0:18:05 So I understand that sort of the verticalized,
0:18:08 specific tasks that the agent is being developed to do.
0:18:09 And then underneath that,
0:18:12 you’ve got this reasoning layer you talked about,
0:18:14 which is the LLM.
0:18:14 – Yep.
0:18:17 – And so when you’re talking about the reasoning layer
0:18:20 needing to get better, correct me if I’m wrong,
0:18:23 but my understanding is you’re not building foundational models
0:18:27 from scratch, but you are doing a lot of work
0:18:30 to kind of fine-tuning them the right way to say it.
0:18:31 – Exactly right.
0:18:34 – To get them to perform how you want them to
0:18:36 in conjunction with your system.
0:18:38 So all this is getting to my question,
0:18:42 which is, is there a lot that a company like yours can do
0:18:45 to kind of tweak the reasoning layer?
0:18:49 Or is it mainly a matter of waiting for the,
0:18:51 the various companies of the world
0:18:53 who are releasing foundational models,
0:18:56 is it kind of more like waiting for them
0:19:00 to ship something better and then you can,
0:19:02 or is it kind of a combo of both?
0:19:06 – No, there’s a ton you can do on the fine-tuning side.
0:19:09 And also what we call on the tooling side.
0:19:13 So traditional AI now is referred to almost as tools
0:19:17 and the LLMs can combine with these tools
0:19:20 and the fine-tuning that you’ve put on top of the LLMs
0:19:23 to create this kind of hybrid structure.
0:19:25 And you touched on it and then it’s an important point
0:19:30 to emphasize in domain specific workflows,
0:19:34 you don’t want randomized outputs.
0:19:38 You want predictable, accurate outputs.
0:19:42 And so depending on your domain,
0:19:46 you need to structure the architecture
0:19:50 so that you might even choose to kind of hard code
0:19:55 or create specific layers that do specific tasks
0:19:57 and then specific layers that create,
0:20:00 that do reasoning tasks or do error resolution tasks
0:20:03 or do input detection tasks.
0:20:07 And so our view, and you asked about kind of all developers,
0:20:08 so I’ll maybe touch on that.
0:20:13 Our view is that there are always going to be
0:20:18 application layer architecture that’s going to be needed
0:20:22 to create a vertical specific use case
0:20:24 for a particular enterprise.
0:20:27 And that’s going to be always really valuable.
0:20:29 And again, you’re kind of riding on the wave
0:20:32 of the LLMs becoming smarter because then you need to do,
0:20:35 your value add can be more and more
0:20:37 and you can scale faster and faster.
0:20:39 But there’s a ton you can do,
0:20:42 even as a small company like ourselves,
0:20:45 to create that domain specificity,
0:20:47 which is hugely valuable.
0:20:51 – So your technology, and not that Sonic Jobs
0:20:56 is focused on anything besides the talent acquisition industry,
0:20:59 but is the approach you’re taking,
0:21:01 could it serve as a framework,
0:21:06 even just conceptually for kind of similar work
0:21:07 in a different domain?
0:21:10 – Yeah, I would answer your question in two ways.
0:21:14 One is we think that as you get better
0:21:16 at a specific domain and vertical,
0:21:18 it actually gives you the springboard
0:21:21 to potentially explore other domains,
0:21:23 which we think is interesting in a way
0:21:27 that we think is more valuable and more likely
0:21:30 to be more successful than starting as a generalist
0:21:32 and moving to being a generalist,
0:21:33 actually starting at a vertical
0:21:35 and then moving out to other virtuals
0:21:38 we think could be more fruitful.
0:21:41 The second thing is, and it’s a little bit of a,
0:21:43 as I mentioned, I’m new to Silicon Valley,
0:21:48 so I’ll say how I see it, obviously, as someone new.
0:21:52 I think AI and AI agents today
0:21:54 have been largely built by people
0:21:57 who are excited about the technology
0:21:59 and looking for a problem.
0:22:02 I think there’s a lot of room,
0:22:05 and I think the other group is the group of people
0:22:08 that have a problem, whether that’s a B2C problem
0:22:12 or a B2B problem, and then look at AI
0:22:15 as creating a solution that could never be created before.
0:22:19 I think the way we’ve done it is the latter
0:22:22 in that we had a problem and we looked at AI
0:22:23 to create a unique use case,
0:22:26 and I think I would encourage anyone listening to this,
0:22:29 and you mentioned that there’s a lot of engineers
0:22:31 that listen to this.
0:22:34 I would encourage anyone with a problem
0:22:38 to know that it’s open and not too often.
0:22:40 It can feel like a wall of garden AI
0:22:42 coming in from the outside,
0:22:44 particularly if you’re not from Silicon Valley.
0:22:47 I would encourage people to lean into the problems
0:22:48 that they have.
0:22:52 – I mean, even at my sort of 50,000-foot level,
0:22:54 what you said just kind of rang true.
0:22:58 Simple as it sounds, that idea of LLMs in particular,
0:23:01 almost by nature, are this semi-black box
0:23:03 or just this emergent capabilities,
0:23:05 all these words that are used to describe
0:23:09 this kind of general process of figuring out what it can do,
0:23:11 and so that approach of starting with,
0:23:13 I don’t know, I personally, if you give me a blank sheet
0:23:15 of paper, I have the hardest time coming up with words,
0:23:18 but if you say, “Hey, here’s what we need,”
0:23:20 so yeah, there’s kind of a similar thing
0:23:22 resonated with me hearing you say that.
0:23:25 All right, got one last question for you before we wrap up.
0:23:27 It’s a little bit of a flip.
0:23:30 We usually like to ask how the work you’re doing
0:23:33 and AI’s impact on the work you’re doing
0:23:35 is gonna affect things going forward,
0:23:37 which we’ve talked about a little bit already,
0:23:42 but since you’re in the hiring space, so to speak,
0:23:44 and we’ve been talking about the inefficiency
0:23:47 of the posting model, changing the conversions
0:23:49 and technology like Sonic Jobs,
0:23:50 increasing that conversion rate.
0:23:53 Again, to put you on a spot here,
0:23:55 what advice would you give to a company
0:23:58 who’s trying to hire right now,
0:24:01 depending on the industry, we’re hearing a lot about
0:24:03 the economy and employment in certain industries,
0:24:07 talent crunches and others, can’t find a qualified person.
0:24:09 So kind of given that broad spectrum,
0:24:12 are there any kind of high level pieces of advice
0:24:14 you’d give to somebody out there
0:24:16 who’s trying to advertise and hire for roles,
0:24:17 but just having a hard time?
0:24:21 – Yeah, I think the simplest piece of advice would be
0:24:26 to go to a job platform, find your job,
0:24:29 and apply for your job on the job platform.
0:24:32 What most companies do is apply for their job
0:24:37 or look at their apply flow on their own company site.
0:24:38 – Oh, but not from the, yeah, okay.
0:24:41 – But you’ve got to remember that most candidates
0:24:43 start their journey on a job platform,
0:24:47 whether that’s LinkedIn or Indeed or Sonic Jobs,
0:24:49 and really think about that experience
0:24:51 from the job seekers perspective,
0:24:55 because if you do that, you’ll optimize your own conversion
0:24:59 and have a better experience for your candidates,
0:25:00 which is pretty cool.
0:25:02 – For folks who are listening
0:25:04 and would like to learn more about Sonic Jobs
0:25:08 from whatever perspective, the technical perspective,
0:25:12 maybe they’re an employer who’s looking for new ways to hire,
0:25:13 or I guess maybe the job seekers
0:25:15 can go look at job listings as well.
0:25:18 Where would you send people to go online?
0:25:20 What all is on the Sonic Jobs website?
0:25:23 And then is there social media as well
0:25:24 that listeners can follow?
0:25:27 – Yeah, so if you’re an employer
0:25:30 and you want to talk more, add me on LinkedIn,
0:25:35 Michael Raja, if you’re a job seeker, AI engineer
0:25:36 that think we’re doing something cool,
0:25:39 we’re hiring, so please reach out to me.
0:25:41 And if you just want to learn about our technology,
0:25:46 we’ve created a page, Sonic Jobs/AI agent on our website
0:25:49 where you can learn more about our technology.
0:25:51 – Great, sidejobs.com.
0:25:51 – Exactly.
0:25:54 – We’re into the age that used to be
0:25:56 way back when it was dot com,
0:25:57 but people would use other ones
0:25:58 if they couldn’t get the name.
0:26:00 And then it was all dot com, org.
0:26:03 Now we’re getting like the dot AI and the other search.
0:26:04 I don’t remember to ask.
0:26:07 So sonicjobs.com, fantastic.
0:26:09 McKeil, I feel like we could talk Asians
0:26:12 and talk the future of the web for longer,
0:26:15 let alone the talent acquisition industry.
0:26:17 But I think that this gives a great purview
0:26:20 into what Sonic Jobs is doing, has been doing,
0:26:24 and also kind of bringing back up the topic of agents,
0:26:25 which is interesting to think about
0:26:28 how things have changed in a short amount of time.
0:26:29 But it feels like a long amount of time
0:26:31 since Jena AI hit the scene.
0:26:34 All the best to you and your whole team.
0:26:35 Thank you for coming on.
0:26:38 And yeah, any words in closing?
0:26:40 – No, thanks very much for having me, really exciting.
0:26:41 – Fantastic.
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0:27:40 [BLANK_AUDIO]
Companies in the US spend $15bn annually on talent acquisition. The most important metric in recruitment advertising is the conversion from the paid click on the job platform to the application the employer receives. Industry-wide, apply conversion is just 5%. Redirection of the candidate from the job platform to the company site is the biggest cause of abandonment; this step has a 70% bounce rate. In this episode of NVIDIA’s AI Podcast, host Noah Kravitz speaks with Mikhil Raja, Cofounder and CEO of SonicJobs, about how they have built AI Agents to enable candidates to complete applications directly on job platforms, without redirection, boosting completion rates to 26% from 5%. Raja delves deep into SonicJobs’ cutting-edge technology, which merges traditional AI with large language models (LLMs) to understand and interact with job application web flows. He also emphasizes the importance of fine-tuning foundational models to achieve more impactful and scalable innovations.
SonicJobs is a member of the NVIDIA Inception program for startups.