Palantir Interview Guide
Palantir’s interview is unlike any other in tech. The Decomposition round tests how you break apart complex problems — not distributed systems, but object modeling and logical structure. The Learning round teaches you something new during the interview and immediately tests whether you can apply it. Add a Karat-conducted phone screen, runtime-optimization-obsessed coding rounds, and a mission-driven behavioral evaluation, and you get a process that rewards adaptability over memorization.
What makes Palantir different
The Anqi Silvia writeup, AlgoMonster's Palantir breakdown, and Jointaro's no-offer retrospective converge on the same signal: candidates who arrive expecting a FAANG loop leave confused. The confusion is the point. Palantir does not run a Google-style loop. The two rounds that matter most — Decomposition and Learning — have no standard LeetCode equivalent. Grinding cannot carry a candidate through. The Decomposition round poses an ambiguous, real-world-ish problem (“design a chess game,” “model a parking lot system”) and evaluates how the candidate breaks it into subproblems, defines data models, and explains reasoning. Two candidates can give very different decompositions and both pass. The Learning round teaches something new during the interview and immediately tests whether the candidate can use it.
The coding rounds are closer to standard, but Palantir pushes harder on runtime optimization than most tracked companies in this set. If an array is sorted, binary search should be the immediate reflex — not something arrived at after a hint. AlgoMonster describes this as “runtime optimization obsession,” and Glassdoor / Prepfully reports track with that framing. The difficulty distribution skews medium-to-hard, comparable to Google and Meta on raw algorithmic complexity but with more emphasis on “can it be faster?” during the problem.
Palantir splits into two career tracks: SWE (core product — Gotham, Foundry, AIP) and FDSE (Forward Deployed Software Engineer, working on-site at client locations). The FDSE interview weights decomposition and the learning round more heavily because the job requires constant adaptation to new client domains. Both tracks go through the same pipeline, but the emphasis shifts. If you are applying for FDSE, prepare to explain why you want client-facing work, not just engineering work.
The behavioral round probes mission alignment more directly than any FAANG company. Palantir's work spans government defense, intelligence, and enterprise data analytics. “Why Palantir specifically?” is not a throwaway question. The strong candidate has thought about the mission, the controversy, and still wants to build there. The weak candidate treats it like any other tech company and gets flagged.
The interview loop
Six-stage pipeline: OA, Karat technical screen, decomposition, learning round, DSA coding, and behavioral/HM. The Karat screen is conducted by a third-party interviewer. Average timeline is 21 days (Glassdoor, across SWE submissions).
Online Assessment
90 min · HackerRankgateThree components: one algorithmic coding problem, one SQL query, and one API design task. 90 minutes total. Problems are LeetCode easy-medium. The API task is unique to Palantir — most companies skip API design at the OA stage. Passing threshold is not disclosed, but community reports suggest completing all three cleanly is expected.
Karat Technical Screen
60 min · Live Coding (Karat)gateConducted by a Karat interviewer, not a Palantir engineer. Brief resume review followed by three progressive coding problems (easy → medium). Tight time budget — you need to move through all three. This is an elimination round: roughly half of candidates advance. Karat interviewers follow a structured rubric, so the evaluation is consistent but somewhat rigid.
Decomposition Interview
60 min · Whiteboard / VirtualgateThe signature Palantir round. Given a complex, ambiguous problem (design a chess game, build a parking lot system, model a supply chain), you break it into subproblems, define data models, and sketch logic flow. Not a full system design — more like low-level design with emphasis on how you think through ambiguity. Pseudocode is acceptable. The interviewer is evaluating your decomposition process, not your final answer.
Learning Interview
45–60 min · Live CodinggateUnique to Palantir. The interviewer teaches you a new concept, codebase pattern, or API — then immediately asks you to apply it to solve a problem. Tests learning speed and adaptability, not prior knowledge. You cannot study for this round directly. The best prep is practicing learning new frameworks or APIs quickly and asking good clarifying questions.
DSA Coding Round
45–60 min · Live CodinggateStandard algorithmic interview. LeetCode medium-hard problems with emphasis on runtime optimization. Palantir interviewers push hard on “can it be faster?” — if an array is sorted, binary search should be your immediate reflex. Clean code, edge case handling, and real-time complexity analysis all evaluated.
Behavioral / Hiring Manager
30–45 min · VideoCombination behavioral + team fit assessment. The hiring manager gauges cultural alignment with Palantir’s mission-driven culture. Expect questions about working on sensitive government or enterprise projects, handling ambiguity in client environments, and why Palantir specifically (not just “a tech company”).
The Decomposition round — what you actually need to know
One hour. Ambiguous problem, no single correct answer. You are given something like “design a chess game” or “build a parking lot management system” and asked to decompose it into subproblems. Define entities, data models, interfaces between components. Pseudocode is fine. The interviewer is evaluating your process: how you handle ambiguity, how you identify the right subproblems, and whether your decomposition is clean enough that someone else could implement each piece independently.
How to prepare: Practice breaking down real-world systems into object models. Draw entity-relationship diagrams. Define clean interfaces between components. Study low-level design problems (chess, elevator, vending machine) and focus on the decomposition process, not the final code. The best candidates explain their reasoning out loud as they identify each subproblem.
Difficulty breakdown
Medium-to-hard skew with aggressive runtime optimization push. Palantir interviewers actively ask “can it be faster?” during the problem. 3.3/5 difficulty on Glassdoor — above Meta (3.1), comparable to Google (3.4).
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Unlock with ProNew grad guidance
Palantir hires new grads into both SWE and FDSE tracks. The interview process is the same full pipeline — OA, Karat, decomposition, learning round, DSA, behavioral — with no shortcuts. This is more thorough than most FAANG new-grad loops, which often skip system design entirely.
The Learning round is where new grads have an advantage: you are used to learning new things quickly. Lean into it. Ask good clarifying questions, read the provided material carefully, and talk through your reasoning as you apply the new concept.
Entry comp is $155K–$190K TC, below Meta E3 (~$305K) and Google L3 (~$250K) but competitive within the defense/data-analytics peer set. PLTR stock vests over 4 years and is volatile — evaluate the base salary, not just the headline number.
FDSE vs SWE for new grads: FDSE gives you broader exposure (client domains, rapid context switching, direct user impact) but less depth in core engineering. SWE gives you depth in Palantir's platform stack (Gotham, Foundry, AIP) but less client exposure. Neither is objectively better — choose based on whether you want breadth or depth in your first two years.
FAQ
What is the Palantir Decomposition interview and how is it different from system design?
The Decomposition round is Palantir’s signature interview format. You’re given a complex, ambiguous problem — "design a chess game," "model a parking lot system" — and asked to break it into subproblems, define data models, and sketch logic flow. Unlike a traditional system design interview, it is not testing distributed systems, scalability, or infrastructure. It’s testing how you think through ambiguity: can you identify the right subproblems, define clean interfaces between them, and explain your decomposition process? Pseudocode is fine. The process is the product.
What is the Palantir Learning round?
A format unique to Palantir. The interviewer teaches you a new concept, API, or codebase pattern during the interview, then immediately asks you to apply it to solve a problem. You cannot study for this round. It tests learning speed and adaptability — how quickly can you absorb a new idea and produce working code with it? The best preparation is practicing learning new frameworks or APIs from scratch. Ask good clarifying questions and read documentation aloud as you work through it.
Does Palantir use Karat for technical screens?
Yes. The technical phone screen is conducted by a Karat interviewer (not a Palantir engineer) following a structured rubric. Three progressive coding problems, easy to medium difficulty, tight time budget. Roughly half of candidates advance past this stage. The Karat format means the evaluation is consistent but somewhat rigid — the interviewer won’t go off-script to explore your thinking the way a company engineer might.
What’s the difference between SWE and FDSE at Palantir?
SWE (Software Engineer) works on Palantir’s core products — Gotham, Foundry, AIP — from Palantir offices. FDSE (Forward Deployed Software Engineer) works on-site at client locations, customizing Palantir’s platforms for specific customer problems. FDSEs need stronger communication skills, comfort with ambiguity, and the ability to learn client domains quickly. The interview process is similar but FDSE loops emphasize decomposition and the learning round more heavily. Comp ranges overlap but FDSE median is slightly lower ($215K vs $237K) with higher variance at senior levels ($330K–$415K+).
How hard is the Palantir coding interview compared to FAANG?
Palantir’s difficulty is medium-to-hard, comparable to Google and Meta on algorithmic complexity. The distinguishing factor is Palantir’s obsession with runtime optimization — interviewers actively push "can it be faster?" during the problem. If an array is sorted, binary search should be your immediate reflex. If a solution is O(n²), they’ll ask for O(n log n). Community reports rate it 3.3/5 difficulty on Glassdoor, which is slightly above Meta (3.1) and below Google (3.4).
What level do new grads enter at and what’s the comp?
New grads enter at SWE or FDSE depending on track preference. SWE comp starts around $155K–$190K TC (base ~$120K–$145K, stock ~$30K–$45K/yr in PLTR RSUs). This is below Meta E3 (~$305K) and Google L3 (~$250K) but comparable to defense/data-analytics peers. Palantir’s stock component has high variance — PLTR is a volatile public stock. The interview process is notably thorough for new grads: OA + Karat + full onsite, no shortcuts.