Distyl AI runs its hiring loop the way its founders learned to at Palantir: hand you a messy, real business problem and watch how you cut it apart. CEO Arjun Prakash managed forward-deployed teams in Palantir’s commercial division before he and Derek Ho started the company in 2022, and the interview carries that lineage. If you have prepped for a standard algorithm gauntlet and nothing else, the loop will catch you off guard, because most of it cares about how you reason through an ambiguous problem, not whether you can reverse a linked list on the first try.
Some context on what the company does, because it explains every design choice in the interview. Distyl sells outcomes, not seats. It embeds its own engineers inside Fortune 500 companies across healthcare, insurance, telecom, manufacturing, and financial services, then ships production AI systems on multi-year contracts, often inside a three-month window. The product underneath is a platform called Distillery that turns a client’s standard operating procedures into auditable agent workflows the team calls Routines. A forward deployed AI engineer here spends real hours in front of a customer’s operations staff, then goes back and builds the system that has to run in their environment, on their data, under their compliance rules.
That model means the loop tests two things a pure coding screen never touches. Can you take a problem stated in vague business language and break it into something buildable? And can you do that while a non-technical stakeholder watches you think? The company closed a $175M Series B at a $1.8 billion valuation in late 2025, led by Lightspeed and Khosla Ventures, with Coatue and Dell Technologies Capital also in. Headcount is climbing, and the bar on those two skills is rising with it.
The shape of the loop
Distyl doesn’t publish a fixed rubric, and the exact number of rounds shifts by role and how busy the team is. What stays constant is the mix: a recruiter conversation, a hands-on coding round, a decomposition round that is the real filter, and an onsite that pairs a build exercise with a values conversation. The structure mirrors the Palantir forward deployed loop the founders came out of, adapted for generative AI work.
| Round | Format | What Distyl is checking |
|---|---|---|
| Recruiter screen | Roughly 30-minute call | Motivation, client-facing communication, whether the embedded-engineer model actually appeals to you |
| Coding round | Live applied Python | Whether you can build a working data-and-LLM pipeline and handle messy real input |
| Decomposition round | Live case with the interviewer playing the customer | Turning a vague business problem into a scoped, buildable plan |
| Onsite build and values | Extended build exercise plus a working-session conversation | End-to-end ownership, judgment on AI output, fit for embedded client work |
The decomposition round is the one that matters
This is the round candidates underestimate and the one Distyl weights most. You get a deliberately underspecified problem, usually framed as a customer scenario, and the interviewer plays the customer. Something like: an insurer wants to cut the hours its claims team spends reading policy documents by hand, so where do you start? The interviewer will not volunteer detail. Your grade comes from the questions you ask and the order you ask them in.
Strong candidates pin down the problem before touching a solution. What does the claims team do today, step by step? How many documents, in what formats, and how often do the templates change? What does a wrong answer cost, and who catches it now? Only after that do they sketch an approach, and they say out loud where it could break. Weaker candidates jump straight to “I’d use a RAG pipeline with embeddings,” which signals they are pattern-matching on tools instead of understanding the work.
A few prompts in the family this round draws from:
- A hospital network wants to flag billing codes that look wrong before claims go out. Walk me through how you’d scope it.
- Our client’s support team answers the same policy questions a thousand times a week. What would you build first, and how would you know it helped?
- You have three months and one operations analyst who can give you two hours a week. What do you cut?
That last one is the tell. The job is delivery under real constraints, and they want to see you make scope tradeoffs out loud rather than promise everything.
The coding round looks like the actual job
Expect Python, and expect it to resemble the day-to-day. Parse a messy CSV or a pile of semi-structured documents, call an LLM API, handle the output when it comes back malformed, and assemble a small working pipeline. Distyl’s stack pulls from OpenAI, Azure, and Anthropic models depending on the client, so comfort calling model APIs and reasoning about their failure modes helps. The bar is less “solve this in optimal time complexity” and more “build something that runs, handle the edge cases a real document set will throw at you, and explain your choices.”
You will still get asked about complexity and data structures where they matter, and you should be able to reason about why a dictionary lookup beats scanning a list. But nobody is going to make the whole round hinge on a hard dynamic programming problem. If your only prep is a few hundred algorithm problems, you are ready for maybe a third of what this loop asks.
What they’re actually screening for
Ownership shows up in every round. Distyl’s engineers carry a client outcome, not a ticket, so the interview keeps probing whether you will chase a problem all the way to production or stop at “it works on my machine.” Bring stories where you owned something ambiguous end to end, ideally where you talked to the people who had the problem instead of getting a spec handed down to you.
Comfort with ambiguity is the second thing, and it ties directly to judgment about LLM output. A recurring theme in these interviews is evaluation. You built a workflow that summarizes contracts, so how do you know the summaries are any good? Candidates who can talk about building an eval set, spot-checking against ground truth, and catching the failure cases that actually matter to the customer stand apart from those who treat a model’s answer as correct because it sounded confident.
Customer instinct rounds it out. These are client-facing roles. The onsite usually includes a conversation that feels less like a test and more like a working session, and part of what the interviewer is reading is whether they could put you in a room with a skeptical VP at an insurance company without it going sideways.
Compensation, with the usual caveat
Distyl doesn’t post salary bands, and as a private company fresh off a large round, its numbers move. What you can say with confidence: forward deployed and AI engineer roles at a well-funded Bay Area Series B tend to pay a competitive base plus meaningful early-stage equity, and the equity is where both the real upside and the real risk sit. Ask the recruiter for the band on your specific level and location early, and cross-check against Levels.fyi and Glassdoor instead of trusting a single figure. Treat any equity number as a bet on the next few funding rounds, not as cash in hand.
How to prep without burning weekends
Get fluent in Python for data and API work: reading ugly files, calling an LLM endpoint, retrying and validating output, wiring a few steps into a pipeline. Practice the decomposition round out loud with a friend playing an evasive customer, because the skill is knowing the right question to ask next, and you cannot rehearse that silently. Read up on how Palantir’s forward deployed model works, since Distyl is a direct descendant and the interviewers think in those terms. Have two or three real stories ready about owning a vague project through to something people used. And be ready to answer the evaluation question for any AI system you describe, because it will come up.
The candidates who struggle here are often the strongest pure coders, the ones who want a problem fully specified before they will write a line. Distyl’s entire business is taking underspecified problems and turning them into systems that run in someone else’s production environment. Show them you are at home in that discomfort, and the rest of the loop gets a lot easier.
Practice the behavioral round:
