Did the Palantir MLE loop for a role on their AI Platform team. This is going to be a long post because I couldn't find anything specific to the MLE track when I was prepping and had to piece it together from generalist SWE posts.
Process: recruiter screen, one take-home (optional but they recommend it), coding screen, onsite with four rounds.
The take-home: a structured ML problem. They gave me a dataset and a problem definition and asked me to build a classifier, analyze where it fails, and describe how I'd improve it given more time/data. I spent about four hours on it. They don't want perfection, they want to see how you think about the full ML lifecycle: framing, features, evaluation, production considerations. Sloppy analysis even with good model numbers is a bad signal.
Onsite round 1: ML design. This is the core round. I was asked to design a system for anomaly detection in time-series operational data. Expected questions: how do I frame the problem, what features, what model families, how do I handle concept drift, how do I evaluate in production without clean labels. This is closer to an ML system design interview than a pure modeling interview. Know your monitoring and deployment patterns.
Onsite round 2: coding. Standard algorithms. A graph problem and a dynamic programming problem. No ML coding at all. Same bar as SWE.
Onsite round 3: applied problem. They presented a vague operational problem and asked how ML could address it. Very open-ended. They're testing whether you'll ask clarifying questions or dive in. Ask questions.
Onsite round 4: behavioral. Mission fit is real. Also a lot around "tell me about a time a model you built had an unintended consequence or was misused by a stakeholder." Have a specific story here.
Comp (senior MLE, remote): offer was 340k total, roughly 210k base, rest in stock over 4 years. Q1 2026.
Palantir MLE is a strong fit if you care about production ML and messy real-world data. If you want to work on pure research or clean benchmark problems, probably not the place.