Palantir Software Engineer Interview Guide

The Palantir Technologies software engineer interview is less dependent on pattern matching than most technical loops, especially outside the coding round. While many candidates prepare through algorithmic drills, that preparation does not map cleanly to the highest-signal parts of the process.

The Palantir SWE interview includes a Recruiter Screen, an Online Assessment, an Onsite (consisting of up to four rounds chosen from Decomposition, Re-engineering, Coding, Learning, and System Design), and a Hiring Manager Final. Decomposition is nearly universal. The Coding round includes approximately 20 minutes of behavioral questions embedded within it.

1. Recruiter Screen - This is a 30-minute phone call covering your background, motivation for joining Palantir, and cultural fit. Palantir filters more aggressively at this stage than most companies. Surface-level answers about working on interesting problems or strong compensation eliminate candidates who would otherwise perform well technically.
2. Online Assessment - This is a 60 to 90 minute HackerRank assessment covering a coding problem, a SQL query, and sometimes an API integration task. Problems are implementation-heavy mini-projects rather than abstract algorithmic puzzles. Python is recommended for speed.
3. Onsite: Decomposition - This is a 60-minute CodePair session and the most distinctive round in the loop. You are given a vague, real-world problem with no defined scope and asked to break it down into testable subproblems while communicating your reasoning throughout. Nearly every candidate receives this round.
4. Onsite: Re-engineering - This is a 60-minute CodePair session. You are given 500 to 1000 lines of unfamiliar code, told what the system is supposed to do, and asked to find and fix the bug. The evaluation focuses on whether your search process is systematic, not on how fast you find something.
5. Onsite: Learning - This is a 60-minute CodePair session. You are given an unfamiliar system, language feature, or API with minimal documentation and asked to figure out how it works and apply it to a problem. The evaluation focuses on how quickly and accurately you build a working mental model from limited information.
6. Onsite: Coding - This is a 60-minute CodePair session covering data structures and algorithms at medium to hard difficulty, with approximately 20 minutes of behavioral questions embedded. Problems favor practical motivations over abstract puzzles. Dynamic programming, graphs, trees, arrays, and hash tables are the most common topic areas.
7. Onsite: System Design - This is a 60-minute CodePair whiteboard session covering distributed systems design at the scale Palantir operates. The round rewards candidates who treat correctness and fault tolerance as first-class constraints and who can reason about trade-offs explicitly.
8. Hiring Manager Final - This is a 60-minute video call covering mission alignment, project ownership, and revisiting areas from earlier rounds where questions remain.

Interview Prep

• Palantir Software Engineer Mock Interview Coaches: 60-minute 1:1 sessions with coaches who have conducted or been through this loop, ending with a hire/no-hire read and feedback by dimension

Palantir Specific

• Palantir Foundry: the commercial platform; knowing what it does and who uses it (Airbus, Ferrari, NHS) is baseline preparation for any "Why Palantir?" conversation
• Palantir Gotham: the government and defence platform; knowing the Foundry/Gotham distinction and the implications of each signals genuine research
• Palantir: Navigating Open-Ended Questions: Palantir's own published guidance on what they look for in the decomposition-style round
• Palantir Engineering Blog: how Palantir engineers think about the problems they build for; relevant for system design and mission framing

The recruiter screen is a 30-minute phone call covering background, motivation, and cultural fit. It is also where Palantir makes its first filter decision, and that filter is not technical.

The recruiter is evaluating whether your motivation to join Palantir is grounded in what the company does and who it does it for. Palantir builds software that touches decisions in healthcare, defence, law enforcement, and financial infrastructure. The people who work there are expected to have thought about what that means. Surface-level motivations like working on interesting problems or technical challenge consistently eliminate candidates who would otherwise perform well in subsequent rounds.

Knowing the difference between Foundry and Gotham is a baseline expectation. Foundry is the commercial platform used by organisations like Airbus, Ferrari, and the NHS to integrate and operate on their data. Gotham is the government and intelligence platform used by defence agencies and law enforcement. Describing Palantir generically as a data company, or mixing up the two platforms, is a signal that recruiters report triggers a quick end to the conversation.

The recruiter also looks for signs of short-term thinking and will probe your career arc for coherence. Prepfully coaches recommend not disclosing salary expectations or the status of other interviews at this stage.

The panel will reject strong technical candidates if they don't seem like a good cultural fit." The recruiter call is where that determination is made first. Coming in with a rehearsed pitch about data and impact is not the same as having actually thought about what it means to build software for Palantir's clients.

If you want a read on whether your Palantir story is specific enough to survive the recruiter screen, a 60-minute 1:1 advice session with a Palantir software engineer coach will tell you where it lands and what to sharpen.

Questions you will face in this round:

• Why Palantir, and why now?
• Walk me through your background and what you have been working on.
• What kind of role are you looking for, and why does Palantir fit that?
• What is your favorite project from your past, and what is your least favorite?

The online assessment runs on HackerRank and typically includes a coding problem, a SQL query, and sometimes an API integration task, completed within 90 minutes.

The problems are not standard LeetCode puzzles. They read more like mini-projects: multi-part implementation tasks where each part builds on the previous one. You need to understand the problem's structure before writing code rather than pattern-matching to a familiar algorithm. This is where Palantir begins testing the decomposition instinct that the onsite will test at length.

Python is the recommended language. The problems are text-heavy, the time constraints are real, and Python's concise syntax saves meaningful time on implementation-heavy tasks compared to Java or C++.

Questions you will face in this round:

• Given a series of financial transactions with account identifiers, timestamps, and amounts, identify all accounts with a non-zero balance and output the balance by account
• A notification scheduling system manages user subscriptions with start dates, end dates, and configurable send schedules. Build the function that generates the correct notification timestamps for a given user
• Given a log file of data pipeline runs with job identifiers, start times, and completion statuses, identify all jobs that exceeded their expected runtime by more than a configurable threshold
• Write a SQL query that extracts the month-over-month change in average rating for each product, given a table of reviews with product identifiers, ratings, and timestamps

The Decomposition round is a 60-minute CodePair session and the most distinctive part of the Palantir onsite. Nearly every candidate receives it.

You are given a vague, real-world problem with no defined scope, no specified inputs, and no obvious solution. Past problems have included designing a chess game from scratch, building a parking garage management system, implementing a social graph with friend recommendations, and designing a system to track the spread of an infection through a network. The problem is not meant to be solved in 60 minutes. It is meant to be decomposed: broken into subproblems, mapped from inputs to business logic to outputs, and worked through collaboratively with the interviewer.

The interviewer is evaluating whether you clarify the problem before you start, whether you identify the natural boundaries between subproblems, whether you handle edge cases by thinking through what inputs could break each component, and whether your communication stays coherent as the problem grows more complex. Candidates who treat this round like a system design interview, reaching for architecture patterns and scalability before establishing what the system needs to do, tend to miss what is being evaluated.

Palantir publishes its own guidance on navigating open-ended questions, and it is worth reading directly before the onsite.

Questions you will face in this round:

• Design a chess game. Walk me through how you would model the pieces, the board, the rules, and the game state from scratch.
• Build a parking garage management system. What are your inputs? What does the business logic need to track? What does the output look like when a car enters, parks, and exits?
• Design a system to track the spread of a disease through a contact network, given a list of individuals and their interactions over time.
• Implement a graph that supports adding and removing nodes and edges, searching for a path between two nodes, and identifying whether the graph contains a cycle.
• Design an in-memory database that supports basic CRUD operations, transaction rollback, and querying by multiple fields.

The Re-engineering round is a 60-minute CodePair session. You are given a codebase of 500 to 1000 lines, told what the system is supposed to do and that the output is wrong, and asked to find and fix the bug.

The instinct that fails most often in this round is finding the first thing that looks wrong and declaring it the problem. Palantir's bugs are logically subtle rather than syntactically obvious. Candidates who fix the first anomaly they encounter often find the output is still wrong because the actual bug is elsewhere and what they fixed was a symptom or a coincidence. The evaluation is not whether you find the bug quickly. It is whether your search process is systematic enough that you can account for why nothing else is broken.

Common patterns in this round include HashMap-based counting operations that double-count under specific input conditions, and if-else chains whose logic is correct for the common case but wrong for an edge case that only appears under specific input sequences. The bugs tend to involve logical errors at the level of data structure operations, counting logic, or control flow rather than syntax or API usage.

The round also evaluates how you approach unfamiliar code before you start changing it. Reading before running, forming a hypothesis before testing, and understanding the original author's intent before modifying anything are the behaviours that score well here.

Questions you will face in this round:

• Given approximately 80 lines of code implementing a contact-tracing algorithm, there is a bug in an if-else logic block causing a HashMap count to be incorrect. Identify and fix it.
• Given approximately 250 lines of code implementing an infection spread simulation across a social graph, there is a double-counting error in the infected contacts logic. Find it without getting lost in the larger codebase.
• A payment processing module has a race condition that produces incorrect totals under concurrent writes. Walk through how you would identify the root cause and propose a fix.
• A data aggregation pipeline produces results that are off by a consistent factor for certain input partitions. What is your process for determining whether the error is in the aggregation logic, the partitioning logic, or the data itself?

The Learning round is a 60-minute CodePair session. You are given an unfamiliar system, language feature, or API with minimal documentation and asked to figure out how it works and use it to solve a problem.

This round does not test prior knowledge of the technology. It tests how effectively you build a working mental model when the information available is limited. Candidates who try to apply familiar patterns from languages or systems they already know tend to get stuck when those patterns do not transfer. Candidates who read what is given carefully, form small hypotheses, test them quickly, and update their understanding based on results tend to move through the round productively even when the material is genuinely unfamiliar.

The evaluation focuses on three things: how accurately you read and interpret new documentation or specifications, how quickly you identify what you do not yet understand and fill that gap, and whether your implementation reflects an accurate mental model of the system rather than a guess that happens to work for a simple case. Edge cases matter here for the same reason they matter in the Re-engineering round: a solution that works for the obvious input but fails for a boundary condition suggests the mental model is incomplete.

Prepfully coaches recommend treating this round as an explicit modelling exercise. Before writing any code, state out loud what you understand the system to do, what you are uncertain about, and what you plan to test first. This narration makes your reasoning visible to the interviewer and also helps you catch misunderstandings before they propagate into your implementation.

Questions you will face in this round:

• You are given documentation for an unfamiliar graph traversal API. Use it to find the shortest path between two nodes in a provided graph, handling cases where no path exists.
• You are given a specification for a custom serialisation format used internally at Palantir. Parse a provided data file using this format and output the result in a standard structure.
• You are given documentation for a simplified query language. Write a parser that accepts queries in this language and returns the correct results from a provided in-memory dataset.
• You are given a new concurrency primitive with documented behaviour. Use it to implement a thread-safe counter that supports increment, decrement, and read operations under concurrent access.

The coding round is a 60-minute CodePair session covering data structures and algorithms at medium to hard difficulty. It is the most conventional part of the loop, but it differs from standard technical screens in one way: every coding interview at Palantir includes approximately 20 minutes of behavioral questions.

The technical portion covers dynamic programming, graphs, trees, arrays, and hash tables. Problems tend to have practical motivations rather than being purely abstract. Candidates who have practised only easy and medium difficulty problems will find this round challenging.

The behavioral portion is a genuine evaluation running in parallel with the technical assessment. It is not a warm-up and not a wind-down. Candidates who arrive with no prepared material for this portion leave performance on the table.

Questions you will face in this round:

• Implement a function to find the kth largest element in an unsorted array without sorting it
• Navigate a maze and return the shortest path to all treasures, given a grid representation with walls, open paths, and treasure locations
• Given a graph representing a network of cities and roads with varying travel times, find the minimum time to visit all cities starting from a given city
• Implement a least recently used cache with O(1) get and put operations
• Given a list of intervals, merge all overlapping intervals and return the result

The system design round is a 60-minute CodePair whiteboard session covering distributed systems design at the scale Palantir operates.

Palantir's clients are not consumer applications. They are government agencies, defence contractors, financial institutions, and large enterprises with data governance requirements, uptime obligations, and serious consequences when systems fail. The round rewards candidates who treat correctness and fault tolerance as first-class constraints, not afterthoughts, and who can reason about trade-offs explicitly rather than defaulting to standard distributed systems patterns.

For senior candidates, the round goes deeper and pushes harder on trade-offs. The interviewer will introduce constraints mid-discussion: what if the data volume doubles, what if this node fails, what if the client needs the data in a different form. Candidates who perform well treat these additions as part of the design problem and incorporate them into their architecture as the conversation evolves.

Questions you will face in this round:

• Design a large-scale data integration platform that ingests data from multiple enterprise sources with different schemas, normalises it, and makes it queryable in near real-time
• Design an audit logging system for a platform used by government agencies, where every action must be traceable, tamper-evident, and queryable by regulators
• Design a distributed data pipeline that processes sensor telemetry from a fleet of aircraft at scale, with requirements around latency, reliability, and data retention
• Design a system that allows analysts at different organisations to query a shared dataset without exposing raw data between organisations
• Design a real-time alerting system for financial fraud detection that must operate with sub-second latency across millions of transactions per day

The hiring manager final is a 60-minute video call and is not a formality. Palantir hiring managers use this round to pressure-test their reads from the onsite, and they are specific about it.

Candidates report being asked to redo portions of rounds where they struggled, being asked about exact metrics and trade-offs from specific projects on their resume, and being asked directly about their biggest career failure. If the Re-engineering round showed a tendency to fix the first thing that looked wrong rather than working systematically, the hiring manager may return to that territory. If the Decomposition round showed defaulting to high-level architecture before establishing inputs and outputs, another decomposition-style problem may appear.

The mission dimension also reaches full depth here. The hiring manager will probe whether you understand what Palantir's software does for its clients, what the ethical weight of that work is, and whether you have thought seriously about being part of it. Palantir describes looking for missionaries rather than mercenaries: people who want to build software that matters to the institutions using it, not people chasing compensation or prestige. That distinction gets tested in this conversation.

For candidates who want a simulation of this round specifically, an online, fully anonymous 1:1 mock interview with a Palantir software engineer coach will give you a hire/no-hire read and targeted feedback on how your project ownership and mission framing are landing.

Questions you will face in this round:

• Walk me through a project in depth: what was the problem, what did you build, what were the trade-offs, and what would you do differently?
• What do you consider your biggest career failure? What did you learn?
• Why Palantir at this point in your career, and what specifically do you want to work on here?
• Where do you see yourself in two to three years within the company?
• Tell me something about you that is not in your CV.

Palantir software engineer compensation varies by level, location, and track. For current base salary and total compensation data, Levels.fyi Palantir Software Engineer compensation data is the most reliable reference. Palantir is known for equity-heavy compensation packages, and total compensation can differ significantly from base salary depending on vest schedules and the current stock price.