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Don't believe the headlines. Hiring competent developers is still really, really challenging. One start-up that's helping solve the quandary for companies is Turing, who - by the way - has already raised $153 million to cure what ails employers. Jonathan Siddharth, CEO and founder at Turing, joins the boys to discuss what makes Turing different, how Upwork gets it wrong, and why pay transparency and equality are still such a hurdle.


INTRO (1s):

Hide your kids! Lock the doors! You're listening to HR’s most dangerous podcast. Chad Sowash and Joel Cheeseman are here to punch the recruiting industry, right where it hurts! Complete with breaking news, brash opinion and loads of snark, buckle up boys and girls, it's time for the Chad and Cheese podcast.

Joel (19s):

Aw. Yeah, what's up everybody. It's your favorite guilty pleasure? The Chad and Cheese podcast is always, I am your co-host Joel Cheeseman joined by the Beavis to my Butthead, Mr. Chad Sowash. And today we are giddy because we have a unicorn on the show. Everybody let's welcome Jonathan Sidarth CEO and founder of Jonathan, welcome to the Chad and Cheese podcast.

Jonathan (50s):

Thank you, Joel. Thank you Chad, for having me. Excited to be here.

Joel (53s):

You sound excited.

Chad (55s):

He does.

Joel (55s):

For our listeners that don't know you Jonathan let's get a Twitter bio on you as a person. What makes Jonathan tick?

Jonathan (1m 2s):

I love building companies and I love artificial intelligence and machine learning. And I love building products that have machine learning at their core. And I sort of cut my teeth in using machine learning to build search engines and autonomous cars. And it's been great to start companies that have ML at their core. That's one part of me. The other part of me is I love cars. I love formula one. I'm also a huge productivity nerd. So I literally have an app on my phone that lets me track whether I am continuously getting better every day. Like I have a few sort of things that I work on to continuously improve and one of my goals is to wake up every day, a little bit better than I was the day before.

Jonathan (1m 48s):

I kind of try to try to work on myself like that. So in a nutshell, it's entrepreneurship, cars and continuous improvement.

Chad (1m 55s):

Just like just like a gen Z. I mean, you talk about things that are definitely something that's not sustainable, trying to get better every single day. Jonathan, you're allowed to have a bad day. Okay. I don't know if you know this or not, but since the pandemic, we've all been able to embrace remote work and being able to have bad days. So this is Chad and Cheese letting you know, you don't continuously have to be better.

Joel (2m 19s):

I love his answer. It's much better than Wordle, walks on the beach and Marvel Comics like the cars thing was awesome. I like that.

Chad (2m 26s):

No, I love the let's dig into that real quick. So how did you get into actual autonomous vehicles? Say that that is incredibly exciting, especially when we start seeing them rolling around.

Jonathan (2m 37s):

Yeah. Yeah. I mean, when I was in, my love affair with autonomous cars probably started when I was in middle school or when I saw Knight Rider. I mean, did you guys see that?

Chad (2m 49s):

Oh God yeah.

Joel (2m 50s):

Hell yeah.

Chad (2m 51s):

Hasselhoff. Are you kidding me? That's David Hasseloff.

Joel (2m 54s):

We watched that in prime time.

Jonathan (2m 57s):

I mean, I saw like reruns of that. It was pretty, I mean it is obviously an old show even back then, but it combined two of my passions, like machine learning, like building sentient systems and cars. And when I was in college, like in my sophomore year, I started reading up about AI and machine learning and specifically neural networks. And I published a few papers on teaching a neural network to learn how to drive a car. And that was my foray into self-driving cars. And it was fun teaching the car how to overtake. And I remember like my first sort of simulation where I would give the learning algorithm, these inputs where you put some obstacles on the road and see how it behaves.

Jonathan (3m 39s):

And it was such a rush to see the car automatically learn how to break, automatically learn how to overtake it. It got me excited about machine learning and that's when I decided I wanted to go to grad school at Stanford because Stanford was pretty far along on the self-driving car, a research area under a few professors. And when I came to Stanford, like I switched my interest and I went from cars to search engines again. No, no real connection. And that was that.

Joel (4m 8s):

Sounds a lot like our time at school, Chad.

Chad (4m 11s):

Yeah, exactly right. Oh Jesus. Yeah. Then my time during Stanford. So how didn't the hell did you find your way to HR town acquisition into this space? How did you find your way here? Because autonomous vehicles is a huge leap from where you are today at

Jonathan (4m 29s):

So the story of actually began when I was running my first startup, which was a machine learning based search and recommendation engine. And the year was 2012. And I remember the year distinctly because for the first time, since starting the company out of school, I thought the company was actually gonna fail. And the reason was we were growing really fast on the web. We had about 40 million users and we were looking to raise our Series A and every single investor I met on Sandhill Road in Silicon Valley, turned us down. Every single one. Everyone was looking for mobile app traction, and we didn't have a mobile app. So I had to hire iOS engineers really quickly.

Jonathan (5m 12s):

And I remember looking at the time to hire these iOS engineers and I was trying to recruit engineers from Google, Facebook, Apple, et cetera, and predictably, everybody turned me down at the time and it was costly and hard to hire my co-founder VJ and I decided at the time that if we didn't do something different, the company was gonna die. It was controversial at the time. But at the time, the decision that we made was, we have to look beyond Silicon valley for great engineers. If we restrict our hiring radius to Silicon Valley, we were gonna die. Right? And I was fortunate to find and work with some incredible engineers from Ukraine, Poland, Serbia, et cetera.

Jonathan (5m 56s):

They joined the team. We launched V1 of our iPhone app, which won awards from Apple. It was rated by apple as one of the best apps for content recommendations. Apple invited us to Cupertino to meet with their team because they loved the app. And that was the hook that the, a critical milestone that we needed to raise the Series A, the series, we were able to raise the Series A successfully and the company eventually had a successful acquisition. After selling the company I took some time off to recharge in 2017, as it was reflecting on like, what was the biggest learning from my first startup, it was the decision to go remote, to think beyond Silicon Valley.

Jonathan (6m 37s):

And when I was looking to start my next company, I decided every company's gonna need this. Every company's gonna want to push a button and hire the best people from all over the world, regardless of where they're based. And that's kind of how I found my way to way to And I teamed up with my co-founder for my first startup. And it's been guns blazing growth since then.

Joel (6m 60s):

Always curious about the Genesis of the name. Obviously I assume Alan Turing was the inspiration. Is there a connection there or just an affection or just it was a cute name that was available?

Jonathan (7m 9s):

There's a connection. You're right. Alan Turing is widely regarded as the father of computer science, the father of artificial intelligence, many people know about the Turing test, where you can't distinguish a machine from a human from when you're just interacting across an interface. Yep. And with Turing, we use a lot of machine learning under the hood to automatically find developers, evaluate developers and match them to the right opportunities. And for our customers, it's like magic. That is they push a button and they can spin up their engineering dream team from all over the world. And it's a combination of machine learning and human ingenuity working together where the lines are kind of blurred.

Jonathan (7m 52s):

So it felt like an homage a little bit to the Turing test. So that was reason number one. Reason number two was the Turing Award in Computer Science is the most prestigious award. I mean, it's, it's the Oscars of engineering, if you will. And we wanted a brand that was synonymous with excellence in engineering, we wanted, Turing's a place where the world's best engineers come to build their careers. So, that was another, another reason that worked and the third, most important reason it was a domain name we could afford.

Joel (8m 21s):

Yeah. Now we're talking. Now we're talking and number three should probably be number one. Now for my real question, is to say that there are a lot of solutions for hiring developers is a bit of an understatement to say the least. How do you guys differentiate? How do you cut through the clutter of all the other solutions out there?

Jonathan (8m 40s):

Yeah. Great question. We now live in a remote first world. Every tech company today is in a race to reap the benefits of remote engineering talent. Right? Airbnb went remote yesterday. Yelp went remote, Twitter, Square, et cetera, all went remote first. Even traditional companies like Siemens, Ford, et cetera, have gone remote first. And the reasons are obvious. You get to tap into a planetary pool of developers. You get to tap into previously untapped geographies like Latin America, Africa, Central Europe, Eastern Europe, parts of Asia. I mean many, many times it's much, much more cost effective to if you cast a wider net to other parts of the US, other parts of the world, and not just let's say Silicon Valley or New York, but remote is hard.

Jonathan (9m 22s):

And it's hard for three big reasons. First, it's very hard to build a large enough global pipeline to find truly great people. If you're Johnson and Johnson, one of our customers, and if you wanted 50 Golan developers from LATAM from Brazil, say, then you'd have to build a pipeline of 500 or 5,000 developers to find that 50 and that can be hard for many companies. And second, it can be hard to evaluate the global engineering talent pool. Like for example, if you looked at an engineer from San Paulo, Brazil, you won't see Stanford, Berkeley in their educational background. You're not gonna see Google, Facebook, Apple in their work experience.

Jonathan (10m 2s):

She could be a great engineer. There's just no signal of the resume. So how do you evaluate all of these people from diverse backgrounds without sucking up all of your engineering teams interviewing bandwidth? So that's hard. And third, if you had to ask any engineering manager, what's the hardest thing about working with the remote team? I guarantee this 80% of the time, they'll say it's communication. Communication is hard because time zones are hard. Often the right kind of daily communication, weekly communication, performance management doesn't happen. Often the manager doesn't know if this person's really working. Are they working on the right things? There is not as much visibility into the work being done. If you are open AI, again, one of our customers, you might care about security.

Jonathan (10m 43s):

And security. And security again, can be tough with a global distributor team. So building a global pipeline is hard. Evaluating them is hard. Security is hard. And your question that are all these other places to hire, like, what's the, how are we different? The difference is if you look at like a recruiting company or a typical staffing company, they don't have global reach and they don't really do any vetting of engineers and most marketplaces. And there are plenty of marketplaces today. They have more of a gig focus. So they attract people who want to do short term projects.

Joel (11m 15s):

So Upwork?

Jonathan (11m 15s):


Joel (11m 15s):


Jonathan (11m 15s):

And you don't find people who want to do long-term engagements and many marketplaces don't really do vetting for engineers. So to find that one engineer, you might have to interview 30 people or 40 people. And if you look at IT services companies like Accenture, TCS, We Pro, Infocetera, they don't have Silicon Valley caliber talent, and they don't have global reach. So we asked ourselves a simple question. Can we solve all of this with software? Like what if we had software that could source engineers from the planet wide pool? Software that could evaluate engineers for a Silicon Valley bar? Software that could use machine learning to match the right developers to the right jobs? And software that could manage the collaboration after the match?

Jonathan (11m 56s):

Software, software, software, software. This is why we built Turing.

Chad (12m 1s):

These three part answers are killing me, Jonathan. So what are you? What the hell are you in the first place? I mean, what I'm seeing is almost like this evolution of a job board and staffing company, you did mention staffing and that they don't have the breadth and reach mainly because they really don't leverage technology like Turing is. So what exactly is your platform? Is it the evolution of staffing or is it the evolution of a job board?

Jonathan (12m 29s):

It's not staffing. It's not job board. It's not a marketplace. Turing's a new animal and we call this category talent cloud. It's a distributed team of developers in the cloud that's sourced by software, vetted by software, matched by software and managed by software. We actually don't fit into a box, which drives a lot of investors crazy because they try to figure us out. Like, are you a job board? Are you a staffing company? And we are neither. We are a talent cloud for software engineers. We predict there be talent clouds for many different verticals, maybe a talent cloud for lawyers or a talent cloud for podcasters.

Chad (13m 2s):

Good luck on that one.

Joel (13m 4s):

No one needs that.

Chad (13m 6s):

So quick question. How do companies pay? Do they pay on the placement? Do they pay on the posting of the job? How does a company actually engage and then pay, for these wonderful, you know, Turing engineers?

Jonathan (13m 18s):

They pay by the month for developers they're working with. So the business model is a staffing business model.

Chad (13m 25s):


Jonathan (13m 25s):

So there's a fixed price. For example, if you're typically a customer might work with, they might come to us and say, Hey, Turing, can you gimme five full stack developers, front end react, backend node and at a tech lead level? They work with the developer for a certain monthly rate. And that's how that works.

Chad (13m 40s):

To me, again, as I dig into this, I know everybody wants to be their own animal, but Jonathan, I'm gonna break it to you here, buddy. You are the evolutionary step, which is good. Dude. Staffing has been around forever. The beauty about what you guys are doing. It's an evolutionary step. It actually is a hell of a lot easier. It happens faster, right? And you can do something that actually has more of a broad reach. So as these investors start asking you, these things, take a look at some of the EBITDA that some of these huge staffing organizations have. And then it's actually not a bad, not a bad model, especially when you superpower it with` technology. Now, next question, you guys got $153 million in funding, right?

Chad (14m 24s):

That was the total.

sfx (14m 26s):

What did you say?

Chad (14m 28s):

Yeah, $1.1 billion valuation. So what are you guys doing with that cash? Are you looking to just get a better, deeper penetration into the US to try to get more jobs from US companies? Or are you guys actually looking to expand? And if you are what countries?

Jonathan (14m 45s):

Yeah. Good question. And most recently we'd opened up a safe at our 4 billion violation cap, which is oversubscribed, which is where the company is today.

sfx (14m 53s):

Pink fluffy unicorn music

Jonathan (14m 55s):

Sorry. I had to correct that.

Chad (14m 57s):

I appreciate that. That's why we have you on.

Jonathan (15m 0s):

The founder in me, like he has to do that.

Joel (15m 5s):

Always be selling.

Jonathan (15m 5s):

What are we using the capital for? I would say the primary use of capital is to build an amazing product that can find the world's best developers at scale. There's a lot of software that goes into Turing to automatically vet these developers automatically match them, automatically manage them through technology. And we are building a self-serve system where we want a person to be able to hire engineers and build their team without necessarily even speaking with somebody at Turing, right? Like almost like AWS, like you spin observers in the cloud with AWS, you should be able to spin up your engineering team in the cloud. So a lot of the money is spent on product R and D.

Jonathan (15m 45s):

And other than that, it's to invest in building the world's best place for an engineer to work at. Like we care deeply about great engineers, great engineering. We do a lot of work on R and D other programs to make sure like engineers feel like Turing's a place where they can grow their career. And we are expanding aggressively. Like we are expanding. Like we wanna support developers from all over the US as well. Like a developer from Kansas should have access to the same opportunity as a developer from Palo Alto,

Chad (16m 19s):

Will they get paid the same?

Jonathan (16m 21s):

They won't get paid exactly the same as somebody in Palo Alto, it would be based on their experience level, their tech stack. And we, we do geo adjust a little bit.

Chad (16m 30s):

Okay. So if they have the same exact, let's say credentials as the individual in Silicon Valley, do they get paid the same?

Jonathan (16m 41s):

We do geo adjust Chad. Honestly, the way it works is we have a conversation with the developer. We ask them, Hey, what will it take for you to work on Turing? And they would quote us a price, right? They would quote us a rate, Hey, here, how much I need to get paid for me to leave my current job and join Turing. And typically what they, what they come up with is something that's 20 to 30% more money than they're currently making. And they get 100% of the money that they negotiate with us when they do that. And we charge customers on top of that to cover our costs. You know, it's not perfect. Like, for example, I'll give you a specific example. If you are working on computer vision algorithms to build machine learning systems on, using 10th of flow, and you are at an engineering manager level so much more senior than an IC, you'll get paid a lot more than let's say a front end developer who's a view JS developer operating at an IC level.

Chad (17m 37s):

Totally get it. The question is around pay equity. The developer community is a bro community, and it is way off with regard to actual pay equity. I'm sure you guys know that. So when you start talking about negotiating their own rates, that automatically sends alarm bells off in my brain saying that more than likely women in the Turing platform are getting paid less than men for doing the exact same job.

Jonathan (18m 2s):

We, to be honest, like we haven't, we don't have data on that right now that would support that hypothesis. For the way we think about it, is we have to make sure that Turing is a step up for that developer that we reach compared to the current opportunities that we have and we are gonna do our best to give them that step up. If we are not able to give them that step up, they won't join us. They'll stay at their current jobs and do what they're doing. We have to earn their trust to come to Turing. And we earn that trust by making sure that they make more money than they're making right now, their career options are brighter than it is right now. And we invest a ton of time in, in resources.

Jonathan (18m 44s):

Like for example, we had a women in tech week a month back, we had a pride week a month back. Our whole company is based on the thesis that talent is universal. Opportunity is not. And we wanna remove all the obstacles that stand in the way from getting people access to the opportunity that their talent deserves. The first obstacle geography, people who won the geo lottery and are here in New York, there are plenty of other obstacles and we wanna level the playing field for people.

Chad (19m 11s):

Okay. So around that, wouldn't it gain more trust from the community and this is what we've heard from developers all over the world, why they use certain platforms is because of transparency and trust. Right? So the trust piece, I totally feel you, but I feel like they will actually trust you more if there was transparency in the system. So therefore we could ensure that we actually have equity across the board.

Jonathan (19m 38s):

How would you and this super interesting, and I'm asking to learn further, if you were building a system like that, like, how would you ensure that transparency? How would you design it?

Chad (19m 47s):

Aggregate data around actual job titles, job titles that have the same requirements, and then you would be able to give the community an idea of what that pay rate would be for that specific type of job and if you met those requirements. Right, we're talking about aggregate data. I'm not trying to find out exactly what Joel's getting paid, but from a transparency standpoint, as we're seeing, I mean, the government is even starting to tell either whether it's state or we're looking at perspectively, federally, that salaries need to be on jobs. Right? So if you can be more transparent and provide that aggregate data? Landscape data, don't you think that would provide more of a push for trust?

Jonathan (20m 31s):

That that's super interesting. And how would you slice that data? Like what columns would you have, like you would have title and salary. Would you have geography as well?

Chad (20m 39s):

Personally? I think if you're doing the same job as somebody in Silicon Valley, the exact same job, there's no reason why you shouldn't be paid the exact same rate. That's my personal opinion. But when we take a look at first and foremost gender, then we also take a look at yes regions so that these individuals will understand if they're in Kansas, they're probably gonna get screwed, but being able to take a look at gender ethnicity, not to mention also, you know, all over the world, the actual, where the developers are at, where they're getting paid, those types of things.

Jonathan (21m 13s):

That's super interesting. Like definitely something for us to think about. Like, we probably have access to some of that data for sure. And do you see any other, like, are there any companies that you, I mean, you, I'm sure you track the space. I'm curious. Have you seen any other company do this?

Chad (21m 30s):

What we're starting to see, is we're starting to see a couple of things with regard to regulation, whether it's starting at the state level first. Right. And then we have companies who are actually leading and talking about equity, which we talked about, and then also transparency. So we're starting to see companies doing this and then driving other organizations like, I think it's Syndio is that the one Joel, where they have, it's actually a pay equity platform?

Joel (21m 58s):

I don't recall.

Chad (21m 59s):

I believe so. Anyway. Yeah, there are plenty within the landscape. This is an issue. Everybody understands it's an issue, especially in the developer community. So to be able to have such a powerful platform like Turing, to be able to help in transparency and equity, I think would be, would be one hell of a step forward.

Jonathan (22m 20s):

Yeah. That's a great idea, Chad, like, I'll definitely look into that. And like, one thing that I love about Turing is if you look at, I mean, you spoke a little bit about, you hinted at this a little bit in terms of potential bias in some of these systems, if you look at interviewing today, right? Like interviewing, it's a highly biased, sexist, ageist, in many cases, broken system, it's inconsistent, non-scientific and subject to all sorts of noise, right? Some people have some teams have great interviewers, some people have not so good interviewers. Highly variable And it's sort of a flip of the coin in many cases.

Jonathan (23m 0s):

So I mean, how many great people do we know who flub interviews? Right? like the there's just so much too many issues with that. One of our goals with Turing is to take that sort of manual, subjective, bias ridden process and try to have it be something much more objective data driven, consistent, but through an automated vetting process. And for the longest time, like we actually, we were not even tracking gender like in the early days, when like one of our customers actually asked us, Hey, Turing, like, can you, like, I would really love to hire female developers. Like, can you gimme female developers? And we were stuck at the time because we actually couldn't search our database by gender.

Jonathan (23m 43s):

We weren't even asking for that. We were just asking people for, 'Hey, if you're a react developer, do this coding challenge. Here are some questions. And our system gives you a score, like based on all the data that we see'.

sfx (23m 56s):


Jonathan (23m 56s):

So we are, I would say there's a long way to go to build a more equitable fair system. And I think as an industry, we can do a lot better and we are so early on and even Turing can do a lot better. Like the, but we wanna take some baby steps towards the future and just make this a little bit more standardized, consistent and data driven.

Joel (24m 17s):

All right. All right. We may not get to the future if we don't pivot to something else here. You talk a lot about how developers want careers. You've said it in this interview and your website touts it. They want careers and not gigs. Do you have any data to back that up or is it mostly anecdotal?

Jonathan (24m 35s):

We have a ton of data to back that up. Like we do these town halls where we ask people what they want in a platform like Turing. If you look at it like a hierarchy of needs at the base of the pyramid, what people want is more money and greater financial stability. They don't want to be hunting for gigs constantly. A developer does not want to do marketing. And unfortunately on many marketplaces, that's kind of what they have to do. They have to keep working around. They would like some support there. Developers historically also don't love interviewing. So often many developers are stuck in jobs that they don't particularly like, because they just don't wanna go through the hassle of preparing for a couple of months, interviewing 10 places and joining.

Jonathan (25m 20s):

So they loved a system where you get vetted ones. And then there is a team that's working with you to help you get matched continuously so you don't have to worry about that. And one level above is they care about working on interesting products with the latest tech stacks. If you're a machine learning engineer, you wanna be working on TensorFlow, PieTorch, you wanna be solving computer vision problems, speech recognition problems, deep use building, deep learning systems, things like that. So they care about the interestingness of the work. That's one level up the pyramid. One more level off the pyramid is they wanna work with great people. And we are so fortunate in Silicon Valley, New York, places like that. We are surrounded by great tech companies working with great people, but you, if you were born in, you know, a small town, a hundred miles from Buenos Aires, Argentina, there might be two tech companies where you are.

Jonathan (26m 12s):

And they love that with platforms like Turing, they get to work with companies like Johnson & Johnson, Rivian, Coinbase, Opening AI, like all of these, Pepsi, these well-known companies. So they love that they're working with great people who again, help accelerate their career. And one more level up the pyramid is engineers love getting better at their craft. They want to keep getting better, keep improving. And they like that at platforms like Turing, we give them access to resources to help improve their soft skills, help improve their communication skills, help improve their interviewing skills, help improve how they talk about themselves in an interview, build a resume.

Jonathan (26m 52s):

We do mentorship like on how you can become from an IC, how can you become a tech lead? How can you become a tech lead manager? And these are all things that engineers at Google, Apple, Facebook, Amazon used to have access to. We are just democratizing it and giving it to everyone in the world. Gotcha.

Joel (27m 9s):

Gotcha. So you mentioned Coinbase and on a weekly daily basis, we hear about tech companies like Coinbase, as well as Netflix, Carvana and a ton of others, that are laying off developers. How has that impacted your business?

Jonathan (27m 23s):

So the short answer is so far, we haven't seen a huge impact yet. However, we anticipate that there will be an impact over the next six months, 12 months, maybe more. I don't have a crystal ball. Nobody does. I think, I mean, from what I hear is, you know, you see companies everywhere, basically focusing their energy on fewer products, fewer projects that are more needle moving for the business. Companies are probably pausing, nice to have initiatives. And I think during times like this, like platforms like Turing will be even more important to help amazing developers find jobs, help people who are displaced find jobs and get back to work.

Jonathan (28m 8s):

Yeah. So the short answer is we haven't seen an impact yet. Although we do, there are some smaller companies that I, some smaller customers we have that have slowed down their hiring, but I haven't seen a big impact yet, but we expect to see some impact over the next six months.

Chad (28m 25s):

Well, Jonathan, I have to say that seeing what Turing's doing is literally the future of staffing and technology, as we know it. Finally! So thanks for coming on the show. We appreciate you coming to act to answer the hard questions. Now, if somebody wants to find out more about Turing, where would you send them?

Jonathan (28m 47s):

Please go to And if you're looking to hire front end, backend, mobile, AI, data science DevOps developers, push a button, bring engineers to your team? That will be and you can follow Turing on Twitter at, @Turingcom. You can follow me on Twitter @JohnSidd -S I D D. Thank you, Joel. Thank you, Chad, for having me, both of you are clearly very, very knowledgeable about the industry and, you know, given your experience with job boards like Monster and others back in the day, no surprises then. And thank you for having me here and was great speaking with you.

Joel (29m 23s):

Has a CEO ever said, please go to Like, thank you. Oh man, I can't take anymore. Jonathan, Chad, another one in the can.

Chad and Cheese (29m 32s):

We out. We out.

OUTRO (30m 30s):

Thank you for listening to, what's it called? The podcast with Chad, the Cheese. Brilliant. They talk about recruiting. They talk about technology, but most of all, they talk about nothing. Just a lot of Shout Outs of people, you don't even know and yet you're listening. It's incredible. And not one word about cheese, not one cheddar, blue, nacho, pepper jack, Swiss. So many cheeses and not one word. So weird. Any hoo be sure to subscribe today on iTunes, Spotify, Google play, or wherever you listen to your podcasts, that way you won't miss an episode. And while you're at it, visit just don't expect to find any recipes for grilled cheese. Is so weird. We out


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