Palantir software engineering candidates report recruiter screening, live coding, decomposition or ambiguous-problem discussions, and behavioral conversations that probe motivation and reasoning.
$208K
Avg. Base Comp
$300K
Avg. Total Comp
4 rounds
Typical Rounds
2 weeks
Process Length
For a Palantir software engineering interview, prepare to explain your thinking as clearly as you write code. One candidate’s process began with a recruiter conversation about background and interest, then moved to a two-part live coding task: compress a repeated-character string and support decompression. That report describes the question as simple, but a bug in the implementation affected the outcome—so practice completing small programs cleanly, checking edge cases, and narrating decisions rather than assuming an easy prompt is low stakes.
Across other engineering accounts, candidates reported coding, decomposition, learning, system-design, debugging, and behavioral conversations. These reports emphasize explaining an approach, identifying bottlenecks, learning unfamiliar material, and discussing implementation tradeoffs. The recurring preparation theme is structured communication under ambiguity.
Prepare a specific answer for why Palantir and how your experience fits the role; motivation and background appeared in recruiter and behavioral conversations. For coding, rehearse concise implementations with loops, strings, hash tables, and standard data-structure fundamentals while speaking through tests and fixes. For open-ended prompts, state assumptions, identify inputs and constraints, break the work into smaller parts, and explain tradeoffs. Reports are limited and individual paths vary, so treat the sequence as a preparation guide rather than one fixed loop.
Synthesized from 12 candidate reports by our editorial team.
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Real interview reports from people who went through the Palantir technologies process.
The process had five rounds. There were introspective questions throughout, such as why I was interested in Palantir, what I would do if Palantir did not meet my expectations, and what unique quirks I bring. During the hiring manager round there was a decomposition exercise focused on improving emergency systems. A sample technical prompt from that round: design a system to prevent blackouts in a power grid.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Palantir technologies
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Over-Budget Projects | |
| Find the Missing Number | |
| One Element Removed | |
| Level Of Rain Water In 2D Terrain | |
| Words in Encrypted String | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Target Indices | |
| Swiping App Design | |
| Subway Machine Learning Model | |
| Three Indexes Adding Zero | |
| Green Dot | |
| Shortest Path Algorithms | |
| Fixed-Length Arrays: Deletion | |
| Electricity Supply | |
| Generating Discover Weekly | |
| Friends vs Posts | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| LRU Cache 1 | |
| Statistically Significant Test | |
| Game Feature Home | |
| Delivery Online | |
| Call Center Resource Management | |
| Stakeholder Communication | |
| 2nd Highest Salary | |
| Closest SAT Scores | |
| Employee Salaries | |
| Empty Neighborhoods |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter or HR conversation about their background, past work, interest in Palantir, and motivation for the role. Prepare a specific explanation of why the work appeals to you and connect it to concrete experience rather than relying on a generic company-interest answer.
One Software Engineer candidate received a two-part string task: compress repeated characters and implement decompression. Other candidates describe easier coding or HackerRank-style work. Practice writing complete code, checking edge cases, and discussing loops, data structures, tests, and bug fixes aloud.
Candidates report decomposition-style and vague problem exercises in which they defined inputs, split a messy problem into subproblems, asked clarifying questions, and explained tradeoffs. A system-design discussion may also appear, so communicate assumptions and priorities before jumping to an answer.
One candidate reported learning, coding, and behavioral conversations after the screen, while another described behavioral questions around background and fit. Be ready to explain how you learn unfamiliar material, collaborate, own problems, and make decisions when requirements are incomplete.
