Palantir·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Jun 2026

Summary

Palantir coding round where you pick two problems from a menu of topics and walk through the whole thing: assumptions, code, complexity, tests, alternatives. Felt more like a structured exercise than a free-form interview, which I kind of liked but also found weirdly rigid.

Questions Asked (2)

Pick a problem from an algorithms topic area (arrays, strings, hash maps, or graphs), state your assumptions upfront, implement a working solution, and walk through complexity analysis.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The 'state your assumptions' part tripped me up more than the actual coding.

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AI HintsAI Generated

Suggested Approach

Choose a problem you know well from arrays, strings, hash maps, or graphs, and state your assumptions clearly before coding. Implement a clean, working solution and then analyze time and space complexity, discussing trade-offs and potential optimizations.

Pro tip: Palantir values practical problem-solving and clear communication. Verbally walk through your thought process, and if you see multiple approaches, briefly compare them to show you consider trade-offs.

1. Select a Problem and State Assumptions

Pick a problem from the specified topics that you can solve confidently. Clearly state any assumptions about input size, data types, or constraints to set the stage.

2. Outline Your Approach

Briefly explain your planned algorithm and why you chose it. Mention any data structures you'll use and how they help.

3. Implement the Solution

Write clean, well-structured code with meaningful variable names. Handle edge cases and ensure the solution is correct.

4. Test with Examples

Walk through a few test cases, including edge cases, to verify correctness. If needed, trace through the code step by step.

5. Analyze Complexity and Trade-offs

State the time and space complexity of your solution. Discuss potential optimizations or alternative approaches and their trade-offs.

Key Points to Mention

Clarify assumptions about input size, constraints, and edge cases upfront.
Explain your choice of data structures and why they are appropriate.
Write modular, readable code with comments where helpful.
Test with normal and edge cases to demonstrate correctness.
Provide precise time and space complexity analysis using Big O notation.
Discuss trade-offs between different approaches (e.g., time vs. space).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

For the same problem you implemented, write tests covering edge cases and large inputs, then discuss at least one alternative approach and its trade-offs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Edge cases I handled okay but I skipped the 'large input' angle initially and they pushed on it.

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AI HintsAI Generated

Suggested Approach

Start by clearly restating the problem and the solution you implemented, then walk through a structured test plan covering edge cases (empty input, single element, duplicates, invalid inputs) and large inputs (performance, memory, scalability). After testing, discuss an alternative algorithmic approach, comparing time/space complexity, readability, and practical trade-offs in a production context.

Pro tip: When discussing trade-offs, tie your reasoning to real-world constraints like latency, maintainability, and failure modes—Palantir values engineers who think beyond Big-O and consider system-level impact.

1. Restate the problem and solution

Briefly summarize the original problem and your implemented solution, including the core algorithm and data structures used, to set context for testing and alternatives.

2. Design edge case tests

Enumerate critical edge cases such as empty input, single element, duplicates, negative numbers, overflow, and invalid inputs. Explain expected behavior and why each case matters.

3. Plan large input tests

Describe how you would test with large inputs: generate synthetic data, measure runtime and memory, check for stack overflow or timeouts, and consider stress testing with worst-case scenarios.

4. Propose an alternative approach

Introduce a different algorithm or data structure that solves the same problem, and outline its basic mechanics.

5. Analyze trade-offs

Compare the alternative with your original solution on time/space complexity, code complexity, scalability, and suitability for different constraints (e.g., streaming vs. batch).

Key Points to Mention

Boundary conditions: empty input, single element, duplicates, negative values, and invalid types.
Performance testing: time and space complexity, use of profiling tools, and handling of large datasets.
Alternative approach: e.g., iterative vs. recursive, different data structure (hash map vs. sorting), or algorithm paradigm (dynamic programming vs. greedy).
Trade-offs: time vs. space, readability vs. performance, and scalability under real-world constraints.
Testing methodology: unit tests, property-based testing, and stress testing for robustness.
Production considerations: error handling, logging, and monitoring for edge cases in deployment.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.