Three Job Searches, Three AI Roles: What Actually Worked

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For this session, I sat down with three product leaders who just went through a full job search and landed AI roles at very different companies — Ben Dreier, now leading product for creative technology and AI at Netflix; Julia Roberts, now on the product growth team at OpenAI; and Janie Lee, now VP of Product at Abridge, one of the hottest AI-native companies in healthcare. None of them came from AI backgrounds. All of them landed roles where AI is central to the product. They agreed to share what actually happened — the real timeline, the decisions that felt risky, and the things they’d tell themselves if they could go back.

Most job search advice is generic. “Network more.” “The market is tough.” “Just keep applying.” None of that helps you make better decisions. What helps is hearing from people who recently navigated the same market you’re in — and seeing what actually worked. The questions going in were the same ones I hear constantly:

“I don’t have the network to open doors at the companies I’m targeting. How do I even get in the room?”

“How do I position myself for AI companies when I haven’t shipped AI products?”

“Everyone’s building prototypes now — what actually separates the candidates who get hired?”

Their searches looked very different from each other. But the patterns underneath were remarkably consistent — and several of them challenge the conventional wisdom about how to run a job search. Below are the top insights from the conversation. The full episode goes deeper on pipeline building, negotiation, and the tactical decisions behind each search — it’s worth a listen for the stories alone.

Interviews Have Shifted from Storytelling to Live Demonstration

“I’m preparing for interviews by rehearsing my past accomplishments. Is that still the right approach?”

Companies care less about where you’ve been and more about what you can do right now.

If you’re preparing for your next interview by polishing stories about what you did at your last job, you may be preparing for the wrong thing.

At OpenAI, Julia found that over 90 percent of the process was live problem-solving. The cross-functional interviews weren’t behavioral — they were working sessions. “Here’s the problem we’re working on. How would you start?” She was pushed to go deeper and deeper on specifics: what would you build first, what’s the experiment, what are the groups, what’s the hypothesis?

At Netflix, Ben received mid-process feedback that he hadn’t come across as showcasing an “AI mindset” in one of his behavioral interviews. He took that as a challenge. When the take-home assignment came, he went deep on AI-first approaches, engaged with where the technology was evolving, and built in scenario planning for capabilities that didn’t exist yet.

Janie had a whiteboard session on a healthcare topic she knew nothing about — and spent a weekend going from zero to a full prototype and commercialization plan. More on how she did that below.

The pendulum has swung toward demonstration. This is genuinely optimistic for candidates without brand-name pedigrees or deep AI credentials. If you can show how you think, engage with ambiguity, and go deep when pushed, strong core PM skills can get you into top AI roles. But it means the preparation is different. You can’t rely on polished stories. You need to be ready to think on your feet about the company’s actual problems.

AI Fluency Matters — But Not the Way You Think

“I’m targeting AI companies but I haven’t shipped AI products. How much does that matter?”

The signal companies are looking for isn’t “I’ve shipped AI products.” It’s: can this person learn fast, think from first principles, and bring skills we don’t already have?

Consider the facts: Ben is now leading AI-driven creative technology at Netflix. Julia is building product on the growth team at OpenAI. Janie is running product at one of the most prominent AI companies in healthcare. None of them had AI backgrounds when they started searching. None of them were hired because they were AI experts.

What got them in the door was the complement skill — the thing each AI company didn’t have enough of. Julia brought growth expertise that OpenAI needed to think about retention, experimentation, and user behavior. That product skill gap mattered far more than any AI knowledge gap. Ben learned this the hard way: he felt pressure to prove he was “the AI person” and even considered leading with a patent he held. But the conversations that went best were the ones where he dropped the branding and engaged authentically with problems — showing curiosity and admitting what he didn’t know.

This is counterintuitive but worth sitting with: the top AI companies are often looking for strong generalist PM skills over esoteric AI depth. They have plenty of people who understand the models. They need people who understand the product. Landing the role makes you an AI expert soon enough. What gets you there is first-principles thinking and the ability to learn fast.

Go Deep or Go Home — High Agency Is the Differentiator

“Everyone’s building prototypes for interviews now. How do you actually stand out?”

The bar isn’t just effort. It’s the kind of effort that signals you’ll operate this way on day one.

Janie is now on the hiring side at Abridge, and she’s reviewed over 120 interviews in a single quarter. What separates the best candidates from the rest isn’t talent or pedigree. It’s depth of effort.

Prototypes are table stakes. AI tools have made it possible for anyone to spin one up in 30 minutes. The differentiator is what’s behind the prototype. Janie’s own interview experience illustrates this: she had a whiteboard session on a healthcare topic she’d never encountered. She used AI tools for deep research, scoured YouTube for founder talks, found product samples, built prototypes, and put together a full idea-to-commercialization plan over a weekend. Two hours before, she didn’t know what the topic was about.

Now on the other side of the table, she sees the same pattern in reverse. The candidates who stand out are the ones who’ve gone beyond the surface — researched the company’s specific challenges, thought through how they’d actually ship and commercialize the product, and show up having already mentally started the job. The ones who don’t stand out generated something in 30 minutes and called it done.

This is the highest-leverage thing you can do in a job search right now. The tools to go deep are better than they’ve ever been. The question is whether you will.

“Stepping Back” in Title Can Be the Smartest Career Move You Make

“I’m a director right now. Taking an IC role feels like going backwards — but the opportunities at AI companies are mostly IC. How do I think about this?”

An IC role isn’t a lesser version of a leadership role. It’s a fundamentally different type of work.

Julia went from a director leading a large growth organization at Pinterest to an IC role at OpenAI. She doesn’t see it as a step back. As she put it: “An IC role is just a very different type of role than I had before. The constraints of my life at the time were different than I had before.”

All three optimized for learning over level. In a market where AI is reshaping what product work looks like, clinging to your current title may mean missing the roles that actually accelerate your career. The question isn’t whether a role is “above” or “below” where you are. It’s whether the work will make you better.

Know What You Want Before You Search — Or You’ll Waste Months Discovering It the Wrong Way

“I’ve been looking casually for a few months but nothing feels right. Should I just keep going or take a step back?”

The question that unlocks a productive search isn’t “what’s the best version of my current job?” It’s “what do I want in my next role that I don’t have today?”

Julia’s story is the cautionary tale and the success story in one. After nine years at Pinterest, she started looking for roles similar to what she was already doing — leadership positions at similar-stage companies. She got offers. And none of them felt right.

The search stalled because she was looking for a bigger version of her current job. Once she asked a different question — what do I want that’s different? — the answer came quickly: more hands-on, more AI-first. That clarity turned three months of active searching into the OpenAI role.

Ben and Janie had sharper filters from the start. Both identified AI-first as a strict non-negotiable — they wanted to be at a company where AI was central to the product strategy, not a feature being layered on top. With that requirement locked in, the optimization became: best company and largest role within that constraint. Ben used it to evaluate both startups and late-stage companies, asking people inside each one how leadership was thinking about AI as a business strategy. Janie built her entire target list around which companies were truly AI-native and had defensibility.

The framework that emerges: identify the non-negotiable difference first — the thing your next role must have that your current one doesn’t. Then optimize within that filter. Without it, you end up with offers that look good on paper but don’t actually move you forward.

Your Network Is Built Over Years — and the “Luck” Is Highly Engineered

“I feel like everyone who lands great roles knows someone at the company. How do you build that network if you don’t already have one?”

Always seek genuine connections with strong people you encounter. Maintain them over time. That’s what generates the luck you need when it matters most.

The practical advice here is less about your job search and more about what you should be doing right now, regardless of whether you’re looking. Build authentic relationships with people you respect — at your company, at companies you admire, in interview processes on both sides of the table. Stay in touch not because you need something, but because the connection is worth maintaining. Respond to recruiter outreach even when you’re not looking. Keep doors open after things don’t work out.

When you do enter a search, those relationships become warm conversations instead of cold outreach. And they open doors you didn’t know existed.

Ben’s role at Netflix traces back to a cold application seven years ago. He didn’t get the job, but he was so impressed by the hiring manager that he asked to stay connected. That person became a de facto mentor and, years later, connected him to the role he ultimately took. Julia cold-applied to OpenAI, had to cancel the interview, and forgot about it. Eight months later, they reached back out — she was still in their system.

Every one of these stories has a moment that looks like luck. But the luck was generated by years of maintaining connections. For those starting from scratch, Janie’s advice is direct: always do the cold outreach. But give people a reason to respond — share a genuine insight, build something that shows you’ve done the work. If someone learns something new from your message, they’ll take the call.

Follow Genuine Curiosity, Not Prestige

The closing advice from all three converged on the same theme.

Julia: get clear on what makes your next role different from your current one — even if the answer looks unconventional. “Getting comfortable with what I wanted upfront would’ve probably saved me a lot of time.”

Ben: follow the fun, not the prestige. “The motivation is gonna come from being so genuinely interested in the problem to be solved that it feels natural that you’re gonna learn something there.” He called this the single greatest factor when it came down to choosing a job.

Janie: use the tools at your fingertips to go deeper than anyone expects. Put yourself in the shoes of someone who’s already at the company.

The thread connecting all three stories is that the search worked when it was driven by authentic interest — in the company’s problem, in the type of work, in the learning opportunity. That’s the thing that can’t be faked and can’t be replaced by credentials.

The Job Seeker’s Perspective

If there’s one theme running through this conversation, it’s this: the job market has changed, and the candidates who navigated it well changed their approach to match.

• If you’re preparing for interviews, shift from rehearsing stories to demonstrating live thinking. Engage with the company’s actual problems. Go deep enough to show you’ve already mentally started the job.

• If you’re targeting AI companies, don’t try to brand yourself as an AI expert. Bring your complement skill — the thing they don’t have enough of — and show that you can learn fast and think from first principles.

• If you want to stand out, recognize that high agency is the differentiator. The tools to go deep are better than they’ve ever been. Use them.

• If you’re worried about title, consider whether optimizing for learning might serve you better than optimizing for level.

• If you’re unclear on what you want, identify the non-negotiable difference — the thing your next role must have that your current one doesn’t. Then optimize within that filter.

• If you’re building a pipeline, invest in relationships long before you need them. The luck you’ll need later is engineered now.

The market is complicated right now. But the people who navigated it well didn’t do anything magical. They were clear about what they wanted, targeted in how they searched, and willing to demonstrate — not just describe — what they could do.

Have your own career question? Get personalized guidance at Nikhyl.AI — it’s where the questions keep coming, and where I’ll keep sharing what I’m learning.