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Inside the AI Talent Hunt: Strategies for Success and Inclusion | EP.18

Join Ron as he explores the ins and outs of hiring practices in the fast-paced AI industry with KUNGFU.AI’s Director of People, Ryman Stringer.

In this episode, Ron and Ryman discuss best practices for building and nurturing diverse AI teams. Ryman shares valuable insights on identifying top talent that aligns not only with technical skills but also with company culture and values.

Learn about essential screening techniques, the importance of upskilling and continual growth, and strategies for fostering ethical and psychologically safe environments within AI teams. Discover how KUNGFU.AI cultivates a unique culture that emphasizes collaboration, growth, and inclusivity.

Ron Green: Welcome to Hidden Layers, where I explore the people and the technology behind artificial intelligence. I'm your host, Ron Green, and today we're discussing best practices for hiring in the fast -paced AI industry. Joining me is my wonderful colleague, Ryman Stringer. Together, we're gonna explore building high -functioning, diverse AI teams and offer tips for spotting talent that not only possesses critical technical skills, but also aligns with your company's culture and values. Ryman Stringer is a culture and people operations guru with years of experience recruiting and developing AI talent. She leads people operations, HR, and talent acquisition for Kung Fu AI. Her interests and expertise lie in emerging technology, AI ethics, remote -first culture building, DEI, and scaling startups through a people -focused lens. Thanks for being here, Ryman.

Ryman Stringer: I'm happy to be here.

Ron Green: All right, so you've been recruiting AI talent for several years now, about half a decade. Things have changed quite a bit since ChatGPT came out. Let's start off with that. What's the biggest difference you're seeing within the AI recruiting market now?

Ryman Stringer: A lot has changed. I think things have changed in terms of one, I think the biggest, probably salary inflation and AI professionals were already really highly paid prior to the release of ChatGPT, but now the landscape has been really, really muddied. The extremes of the spectrum in terms of salary are quite extreme. So that's a big one. Another one too is the conflation of AI with gen AI, where recruiting talent of individuals who want to come into AI or claim to be professionals in AI or whatever the case may be, figuring out screening questions that say, do you know AI or do you just use a lot of ChatGPT? And figuring out sort of the sort of commoditization of Gen AI, of AI. We already looked for individuals that were very socially conscious, might not be the right phrase, but aware of the repercussions that AI can have. And I think that that has become even more paramount given how ubiquitous AI is throughout the world. And so coming from it from a few different angles, it's changed a lot.

Ron Green: Are you seeing a lot more applicants? And has the quality of those applicants gone up with the interest in AI, or has it gone down? What are you seeing?

Ryman Stringer: It depends on the role. I would say for machine learning engineers, in general, I think the quality has gone up. And you have to also think about education and how colleges are addressing the rise in AI. And so especially that more entry-level talent, you're seeing a lot more professional education. I would say for on the other side, the quality has kind of gone down because now AI is the thing. Everyone wants to work in AI. And I do actually think it's important that everyone tries to get their foot in the door in AI because I do believe this is the future, a la the internet. But say I'm hiring, I'm gonna say a role that we don't hire. Say I'm hiring a marketing director. We have one, she's great, we're not hiring that. But say I'm hiring that, right? The amount of applicants we get who want to be, say a marketing director at an AI company is really, really high, and therefore the quality does sort of go down.

Ron Green: Okay. Yeah. All right. So we talked a little bit about this earlier, but how do you differentiate in this really competitive environment, a field that's growing pretty rapidly, how do you differentiate to attract talent, top talent especially?

Ryman Stringer: So, and I think this also sort of ties into the salary point I made earlier. We need to have a very firm point of view on culture and what our team is and what we offer, we don't offer the salaries that say fang companies or whatever the new acronym is based on all the mergers and acquisitions that I'm not aware of, but we don't offer those, right? And so having a really firm point of view on culture and who fits in here at KUNGFU.AI is really, really important. And so how I make that differentiation, my recruiter and I, Joop, we tend to focus on four different things in our screens. And if some individual is not excited by any of these four things, they're probably not a fit. We focus on ethics. So we have dedicated time as a company to discuss these things as a team. You have carte blanche to throw any ethical red flags, yellow flags, etc. We focus on culture and collaboration and psychological safety, which I know I think we're probably gonna talk about later. But creating a sense of belonging, what culture means to us in the role that the individual plays as a cultural steward in the company. We talk about a focus on upskilling and continual growth. So the rate of progression in AI research is mind boggling and dizzying. It is. And so having a focus on that, making sure that folks are interested in upskilling is important. And then finally, the variety of work that we do. So having a breadth of experience, being exposed to lots of different things, not just saying I wanna do LLM work and being really excited about, again, any of those four things I just mentioned. Therefore, people kind of self -select in or self -select out. And so differentiating, for me, is very specific to the company. But I do think that that should be how it is for anyone recruiting in AI. You need to have a really firm point of view of what you offer. These engineers or these folks, and who kind of fits in and makes the team that you wanna build.

Ron Green: I'm kind of curious, if somebody is not interested in one of those four, what are they typically interested in then? Are they just interested in...

Ryman Stringer: I can't peg it. I don't know. Sailing? I don't know. You know, it's.

Ron Green: They're just looking for a job.

Ryman Stringer: they're either just looking for a job or they're just not the right fit, you know, and that's okay. And I think that's sort of our perspective is it's almost kind of like dating or finding friends, right? Not everyone's for everyone. And so finding people that really are excited about the same things you're excited about is what's most important. And if they're excited about something that we don't necessarily focus on, they're not going to fit another company either, right?

Ron Green: approach it. Okay, okay. I think we've all been through, you know, recruiting processes where you feel like you've just been abandoned in the middle of the pipeline. And that's something that I know that you and your team focus on really, really hard. So let's talk about the process and how once you've identified top talent, how do you move them through that whole hiring experience?

Ryman Stringer: Yeah. So it's funny, I have friends send me like HR memes or recruiter memes where it's like, HR is the enemy. HR is not your friend. Recruiters are ghosting you. And a lot of people have had terrible experiences with recruiters. I myself have had bad experiences with recruiters. That's just sort of the name of the game. So what we really focus on, we don't treat our candidates any different than we treat our employees. We care about our employees. We want to make sure that they're successful. Our candidates are no different, even if they don't ever join the company, right? And so how do you show care throughout the process? One, talk to them frequently. Two, be honest and transparent. This is who we are. I want to get to know you. I want you to get to know us. And I want this to be a mutual decision, right? It's a two -way street. There's just as much a chance that you're not interested in us as we're not interested in you. And let's just be really honest about that and have that conversation. Understanding that there are a lot of factors that go into your decision, right? Where even if you're a great fit and we're a great fit for you, maybe it doesn't work out for your spouse because they need to move or we try to have a really flexible work -life balance. But there's so many different things that come into play in someone's life, making a decision like a job, right? It's the majority of your life, huge. We really want to be aware of the holistic view of the human and making sure that we show that care. And therefore, we also kind of expect that care back and that honesty back. And so having that sort of reciprocosity, is that a word?

Ron Green: Yeah, you know it.

Ryman Stringer: Nice. Having that reciprocosity is really important.

Ron Green: Okay, and I hear all the time from people who've gone through the process, even people that maybe didn't land the job that it was really maybe the best experience they'd ever gone through from a recruiting perspective because you and the rest of your team just are constantly communicating. You treat them with respect. You don't abandon them. And that's just really, really key, and I think it's like that first impression that you get that may stick with you throughout the rest of your experience, right? Yeah. All right, I know you believe really strongly, and I share this belief that prioritizing ethics and psychological safety is a really big deal because we're trying to build high -functioning teams that are doing leading -edge technical work in AI. But how does that contribute? Like, why is that important?

Ryman Stringer: So, one thing I've learned working in AI is the techniques are going to change, the technology is going to change. What's not going to change is how you talk about those techniques, right? How you talk about bias in historical data, how you're able to approach difficult conversations around how does a zip code actually, even though it doesn't have PII, how might that show demographic data, right? And having those difficult conversations and being able to lean into them and also recognize that you are not going to be the only one that knows all of it and the only one that can throw flags because of different lived experiences is just vital, right? People can learn the techniques, people can learn the technology. It's so important that we hire people that understand how this can actually impact humans. It can impact your society, your community, the world as a whole. That is just paramount and that's a lot harder to teach than, you know, here's how to use a Jupyter Notebook, right? It's just...

Ron Green: Yeah, I feel like because we're working in AI and machine learning, the power of these techniques and the things that are increasingly being automated, you're talking about like loan decisions and things like that that are really, really fraught with risks of bias and other negative aspects, it's really, really critical. And I think it's something that, again, you and your team index on really, really hard. Okay, I want to talk a little bit about advice for companies that are, let's say just starting out, they're trying to build and they're trying to hire AI talent. How do you develop the culture in the first place? Like how do you get that nugget of culture in place so that you can grow a team and recruit a team in this really, really fast paced competitive AI industry?

Ryman Stringer: Yeah, I think touching on kind of what I mentioned earlier, having a really firm point of view on what sort of culture you want to have and how that ties into the current AI landscape. So how do you focus on upskilling? And this is especially coming from an HR director's perspective, what support systems and frameworks you have in place in your current team that say, here's how we do L &D, here's how we do upskilling, here's how we do continuous education. Having an understanding of how do we actually screen for the technical ability, right? And then how do we, how often do we need to be updating those questions? Because again, everything's moving so, so fast. Having a very great relationship with your hiring managers and understanding what needs to go into that, having a great relationship with your interviewers, having the rest of the team understand the role that they play in recruiting. These are all not necessarily specific to AI. I think this is just for good recruiting practice in general, but specifically to AI, understanding the framework in which this team is going to function and how it's going to keep up with the rapid rate of progression and also understanding how that plays into your compensation philosophy and do your benefits because that's a whole other can of worms, so. I could probably wax poetic about this, but I'll keep it there.

Ron Green: One of the things that, you know, you and I talk about a lot are, you know, DEI, right? That can be really tricky sometimes, because you may say, like, look, we want to diversify along some specific dimension, right? We're just huge, huge believers in this at KUNGFU.AI. But that can be hard. So, like, do you have any tips for how to go about that? Like, do you just, do you just, just hope that it happens? Are there specific things you can do to make it more likely to succeed there?

Ryman Stringer: There are so many specific things you can do. You know, here we even go so far as when we write a job posting. I love job postings. I feel so strongly about job postings. One, we run them through a gender decoder. Sounds bizarre, but what we do is, is this masculine coded? Is this feminine coded? OK, and I can say as a woman who's applied to jobs, if I see on a job posting, we're looking for a rock star ninja. Right. Yeah. We're looking for someone who like wants to be challenged, you know? Yeah, that's, you know, I'm not super interested in applying to that job. Now, if I see something that says we're a team that empowers you to continually learn, we're a team that wants to know what you know, we're a team that prioritizes knowledge, share things like that, a lot better, right? I'm a lot more likely to apply. I also in job postings, you know, you see the sort of stereotypical bullet point of excellent communication skills. So what does that actually mean? So when we write our job postings, I take more of a storytelling approach because I want an individual to be able to picture themselves in the role. What are you doing day to day? What do you mean you have excellent communication skills? Right. Why does that matter here? Right. So at KUNGFU.AI, you're going to be working with a variety of different clients. You're going to be working with a variety of audiences within those clients. Technical, non -technical, senior, not senior, manager, not manager. How are you able to actually tailor your communication? Is that something that excites you? If it's not, maybe not the role for you. That's OK. So storytelling, making sure that individuals can actually see themselves in the role makes it more accessible to a wide range of audiences. Right. Because I'm not necessarily looking for one specific type of person. I'm looking for someone who is excited by this sort of thing. OK. Another thing that we do is making sure we're posting to a really wide range. We're looking at, you know, women in tech job boards. We're looking at those sort of things, sort of the standard diversity group, recruiting and then making it a talking point during the interview process. This is something that we prioritize. This is something that we have conversations about. It's not something that we shy away from. And it's something that we're trying to improve. Right. And again, it makes people sort of self -select in or out because if that's not important to them, then then they will self -select out. And if it is, and I can tell you as a woman going through an interview process, if I was talking to a recruiter and they said, hey, you know, we don't have enough women on our team and it's something that we're working on. But here's how we prioritize it while we're working on it. Right. And here's how we create a culture that you will feel safe in as we're working on it. And it's important in AI for all the reasons I mentioned earlier, where I'm going to notice things that you're not going to notice. You are going to notice things that I'm not going to notice, not because we're at fault, but because we have lived completely different lives. And so having a wide range of lived perspectives and experiences is just so important.

Ron Green: What about, you've mentioned a couple of times, like, continuing education. I know that's really important to everybody at the company, but how often do you see that being, you know, kind of a major focus point through the recruiting process?

Ryman Stringer: a lot. You know, we, we talk about it a lot. And again, if someone's not excited by the idea of like, you know, say lab day where we dedicate time to upskilling, mentorship, et cetera, that's, that's tough.

Ron Green: Maybe describe Lab day a little bit more. Because I think that's actually a really important recruiting point, and this is something that other companies may want to borrow and implement.

Ryman Stringer: Yeah, and if your company can afford to do it, I highly recommend it for AI teams. It's essentially every Friday, we allocate the full day to upstilling. And it is pretty autonomous. Like, I'm not the one, I'm not the arbiter of what happens on lab day. But we ask our engineering team, we ask our delivery team, we ask our strategy teams, put things on the calendar. What do you want to learn about? What do you want to share? Here's your dedicated time, you can use it as you will. But you have that time to learn.

Ron Green: Right. Read white papers. Read white papers. Product time.

Ryman Stringer: have discussions about this one our ethics discussion group is you know we do cross -functional mentorship so are you learning about you know I've mentored one of our engineers in HR she mentored me in Python right so having that dedicated time is really important I mean it is a big big selling point as well

Ron Green: How is recruiting within the AI industry different than your experiences recruiting more broadly for more traditional software roles? Is it really that distinctly different?

Ryman Stringer: I think it is, and maybe it's, you know, is it substantially different from, you know, recruiting software engineers versus UX versus, you know, I don't want to comment on how big that disparity is, but I do think that, again, there's such a focus on sort of the social sciences aspect of it. I really think it's important for machine learning engineers to be well versed in discussing ethical issues and discussing bias, not shying away from it. I don't know. Sometimes you can hire a software engineer that's happy to just be heads down coding and that's all they do, right? I just don't think that you can do that as a machine learning engineer, and maybe that's a hot take. But I agree.

Ron Green: I don't know. I agree. I think, you know what I think it is? It's a combination of a few things. Things are moving so fast. Yeah. If you're not like dedicated to reading, you know, and staying up to date, you're probably going to fall behind. And I think the other part of it is the recognition that like we talked about earlier, these techniques are increasingly powerful and they're being used to automate things that were just unimaginable until recently. And so the power that comes along with that really, I think, has to be taken seriously. And if you have that traditional software focus where I'm just building, you know, some sort of website, I really think you're not going to thrive in most AI, high -function AI environment teams.

Ryman Stringer: especially, I mean, look at LLMs, not only is the rate of progression really, really fast, it's getting bigger and bigger and bigger. And the explainability is going down. So it's like, yeah, you have to have that ability to have conversations, um, and have an understanding of it and an interest in it, right? I think that might be the biggest difference. It's not just an understanding. I think you need to be really interested in it as an MLE, uh, to be really high performing and on a high performing team.

Ron Green: Yeah. Any advice you would have for people that may see this or thinking about applying and joining KUNGFU.AI, what advice would you give them?

Ryman Stringer: one, please apply. I think that's another thing around diversity and inclusion. Please apply. So many people will look at a list of bullet points and be like, I only fit 50% or please just apply. You know, you might not be a fit, right? But take a chance.

Ron Green: See, I love that, I don't mean to cut you off, but I did not expect you to say that. That's fantastic. Yeah. Because that obviously increases the amount of work and review that you have to go through. But too many people think that they may not be able to do it. Right. Right. But it's amazing how many people, when given the chance, really can rise to the challenge.

Ryman Stringer: 100%. And so I always really encourage people to imply. In all likelihood, you know, we don't hire a ton of people, we're a small firm, but so in all likelihood, we won't get the job, but try, please. Because again, there's so many things that people can offer in this space. We're so early in AI. You always say we're at day zero, we're at day zero. We are. So please apply. So that's the first thing I would say. You know, the second thing I would say is apply if you're interested, right? Also be very aware of what we're trying to build here. Be really familiar with the company and make sure that this is something that you would want to be a part of. And that's advice I would give to any job seeker. But in general, also just be yourself. Again, this is a two -way street. I want you to opt in as much as I want to opt into you. So just be honest with yourself. If you're not interested, that's okay. If you're really interested, tell us. Just be open. Be really open.

Ron Green: What about the role of the rest of the company in recruiting? You know, we have a dedicated in -house recruitment team, but you can't do it alone. So we've talked about this before. What is the rest of the company, how do they play, or what role, maybe I should phrase it like this, what role should the rest of the company play if you're trying to build a high -function AI team?

Ryman Stringer: Well, one, I would say I am extraordinarily grateful that I have a seat at the leadership table, right? I have your ear, I have our managing director's ear, and that's vital, right, for building culture, because I can talk about culture till I'm blue in the face, but if I don't have y 'all's buy -in, and the buy -in of all of our team members, I mean, nothing's gonna happen. So one, making sure that HR and recruiting has a seat at the table, I think is paramount to building high -performing teams. And that's not just AI, that's any high -performing team you're wanting to build. Two, we are, when I do our interview training, I train on, here's what not to say, obviously, but also, here is your role. Your role is to sell the company, because the entire recruiting process, I'm trying to make sure that if we offer you, you accept. So at the very, very front of the conversation, of conversation one, here's the budget for the role. Does that fit? We're trying to mitigate throughout the entire process the factors that would opt you out, and then the interviewers are playing a role in selling the company. So if you're really excited about ethics, let's make sure you have all the information about how we approach ethics as possible. And it's so important that our interviewers know that. It's also important in terms of onboarding, and once the person is actually here, it is not just my job to make them feel welcome. It is their manager's job, it's their teammate's job, it's the job of their coworkers who will rarely work with them. Creating that sense of belonging is absolutely vital. I cannot do it alone.

Ron Green: Right. Let's take it to that. Let's talk about the onboarding process, because I don't want to exaggerate, but I've heard numerous times from people that the recruiting process was the best they'd ever been through, and that the onboarding process was just fantastic, and you designed that whole onboarding process. Talk at a high level about best practices there for people joining the team.

Ryman Stringer: Onboarding is fascinating. And I think that's one thing I really like about my career is I just find people so interesting. And having a new person join an already established team, there's so many pitfalls, right? And this person so wants to succeed, how do you take advantage of that? How do you make sure that they're set up for that success? So it's really an interesting thing to design. One, I have sort of the overarching macro onboarding, right? How do they know about our benefits? What is the ease of setting up their direct deposit? How do they get set up with their one -on -ones? How do we set up meetings? What do they need to know about the company? What's my big onboarding deck? That sort of thing. But then what I think is also really important is sort of the micro stuff, especially in a remote first environment. And so I always tell people this on my welcome call, think about Slack and how many different companies you've used it at and how different the culture is across companies. Now imagine you're at a remote company, you come in, you know how that you've used Slack at your last company. And then you come into this new company and it's like, I don't know any of these channels. I don't know how many, do people post in random? Do people react? There's sort of these little cultural nuances that are really, really important. The example when you're in person is like, where's the bathroom? So looking at onboarding from a really macro perspective, but then also looking at it from these little tiny nuances. And I always tell people that are new, I'm not going to be able to cover all of those. There's no way, I've been at the company for almost five years now. I'm not going to be able to know everything that you don't know. So know that you have carte blanche. There's no question too small to ask me, because I know that coming into a remote first environment with a bunch of different tools, with already established teams, there's going to be questions that you have that I cannot foresee and make sure that you know that I am here to metaphorically hold your hand and walk you through it.

Ron Green: And one of my favorite things that the recruiting team does at Kung Fu AI is the way you set up a series of sort of one -on -one meeting groups with everybody because we're a remote first company, it might be the only way you meet somebody until you travel together for some trip and you meet them in person for the first time.

Ryman Stringer: Yeah, we do virtual meet and greets. And so I have a whole template for week one, day one, day two, day three, week two, day five, or whatever, day six. And just as important as the, here's the intro to the project you're on is, here's a virtual meet and greet. You have a virtual meet and greet almost every day. And it's not just your team members. It's, say you're a machine learning engineer, you're meeting the marketing team, right? You're meeting the finance team. Because again, creating that sense of belonging as soon as possible is paramount to someone's success. And so the more familiar faces that there are, the better. We also have folks do something called a personal emblem at every, their first or second all hands, where it's kind of a silly exercise, but it means a lot. Where it's, hey, here's something that you don't know about me. Here's a value that's really important to me. Here's something I'm really good at. Here's something I'm trying to improve at. Creating fodder for connection is vital to a successful onboarding process.

Ron Green: Okay, Ryman, let's wrap up with one of our traditional, just fun ending questions. If you could have AI automate something in your daily life, what would you go for?

Ryman Stringer: Laundry without a doubt about it all, actually laundry. Hard laundry. Laundry. All right, that's my answer. I hate it. And you know what, I don't know how to do it. I don't know how AI would do it. I don't know if there's a robotic system, but frankly, that's not my job. My job is to hire the people that can figure that out, okay? Figure out laundry.

Ron Green: You feel pretty strongly about this.

Ryman Stringer: I hate laundry.

Ron Green: Oh, I'm right there with you.

Ryman Stringer: Yeah I don't separate whites and colors. My whites are so gray.

Ron Green: All right, maybe your mom will stop listening by this point.

Ryman Stringer: Probably. Shout out Liz.

Ron Green: All right, Ryman, thank you so much. This was fantastic.

Ryman Stringer: It was great. Appreciate it, Ron.

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