Forward Deployed Software Engineer (Commercial) Interview Experience

I walked through my background and tied it to wanting to solve real-world operational problems, not just write code in isolation. When I gave a generic answer at first, they pushed me to say what kind of operational problem I actually wanted to work on and why Palantir's model fit that better than a conventional SWE job.

I gave an example of working directly with a user or stakeholder with unclear requirements, and the recruiter kept pressing on what I specifically did versus what the team did. The theme of that round was that they did not want passenger-seat stories.

How comfortable are you spending time with customers and potentially traveling?

I treated that as a real part of the job, not a throwaway recruiter question. The whole screen made it clear they cared whether I was actually comfortable being customer-facing and operating in messy situations.

You are given a collection of jobs. Each job may depend on several other jobs. Implement a function that returns a valid execution order.

I wrote a topological-sort solution with a queue-based approach, then talked through cycle detection, multiple valid orders, and how I would surface all jobs in a cycle instead of just saying one exists. They also had me name abstractions clearly, write a few test cases, and justify why I structured it that way. It felt less like pure LeetCode and more like, could another engineer pick this up and extend it safely.

I started by clarifying the goal instead of jumping to a feature, asking whether we cared more about earnings, idle time, availability, or safety. I proposed a demand-recommendation tool and drew the flow from trip and location ingestion through features, prediction, ranking, and the driver app. When they shifted the goal from accuracy to trust, I added confidence indicators, explanations, dismissible suggestions, and a feedback loop. They cared more about whether each decision tied back to the driver's outcome than whether I named the perfect cloud component.

Take five minutes to read the documentation and explain what you think this code is doing.

I read an unfamiliar Python codebase, a schema, and some query API docs, then explained my understanding before coding. For get_active_customers(start_time, end_time, minimum_events), I talked through querying events, grouping by customer, deduplicating records, and returning only customers over the threshold. A lot of the pressure was around assumptions from the docs, pagination behavior, and where dedup should live. The interviewer would clarify the docs if needed, but they would not choose the implementation for me.

The expected count is four, but the function returns six. Find the defect.

I reproduced the issue with a smaller test case and traced it to the same customer being updated twice through two different relationship paths. I changed the accumulation logic to use unique event identifiers, then explained why that fixed the root cause instead of just satisfying one failing test. They also pushed on whether my fix could throw away legitimate events and what extra tests I would add.

Customers are receiving duplicate risk alerts. The service is not crashing, and the individual functions appear to work. Determine what is happening and fix it.

I traced the duplicate alerts to retry handling plus a non-idempotent state update. A downstream write timed out, the caller retried the whole operation, and a second alert got produced even though the first write had succeeded. I reproduced it with a test, added an idempotency key, and then had to switch gears and explain the bug in customer terms: temporary communication failures could make the system repeat a completed action, so analysts might investigate the same case twice. They cared a lot about blast radius, cleanup, and safe rollout.

Why commercial rather than government-facing?

Add support for compound rules such as (revenue > 1M AND region = "US") OR risk_score > 0.8.

I treated it less like just writing code and more like designing something a customer could live with. I talked through representing compound rules in a structured way instead of growing a giant if-else chain, handling malformed input, keeping enough logging for auditability, and making alert explanations understandable to a customer. They were checking whether I could still keep the operational result in view while making technical tradeoffs.