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Palantir·Software Engineer·Technical Phone Screen·Junior

JuniorPending
Jul 2026

Summary

Did a Palantir SWE intern interview split between behavioral and technical. Behavioral was fine, technical was rough. Still waiting to hear back and genuinely unsure if the coding struggle is a dealbreaker.

Questions Asked (1)

Q1

Given a technical problem, can you brainstorm and arrive at an optimal solution within a constrained time window?

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Needed a hint to get to the optimal approach, which already felt bad.

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AI HintsAI Generated

Suggested Approach

Demonstrate a structured problem-solving process: clarify the problem, discuss brute force and optimizations, analyze trade-offs, and outline an optimal solution with complexity analysis. Emphasize iterative refinement and communication under time constraints.

Pro tip: Verbalize your thought process and explicitly state assumptions; interviewers value clear reasoning and adaptability over a perfect solution. If stuck, simplify the problem and build up.

1. Clarify and Understand

Ask clarifying questions to confirm inputs, outputs, constraints, and edge cases. Restate the problem in your own words to ensure alignment.

2. Explore Approaches

Start with a brute-force solution, then identify bottlenecks and brainstorm optimizations using appropriate data structures or algorithms.

3. Evaluate Trade-offs

Compare candidate solutions on time/space complexity, scalability, and code simplicity. Discuss why one is optimal for the given constraints.

4. Outline Optimal Solution

Present the chosen algorithm step-by-step, including data structures and key operations. Analyze time and space complexity.

5. Test and Refine

Walk through examples, including edge cases, to validate correctness. Be open to feedback and iterate if needed.

Key Points to Mention

  • Time and space complexity analysis (Big O notation)
  • Trade-offs between different approaches (e.g., sorting vs. hashing)
  • Use of appropriate data structures (e.g., heaps, hash maps, trees)
  • Handling edge cases and constraints
  • Iterative refinement from brute force to optimal
  • Communication and collaboration with the interviewer

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