The phone screen wasn't with HR, it was an engineer, which threw me off a bit.
Focus on personal qualities, values, and experiences that shape your identity outside of coding, and connect them to how you approach problems and collaborate. Show how these traits make you a well-rounded engineer who can thrive in ambiguous, team-driven environments like Palantir.
Pro tip: Choose 2-3 authentic stories that reveal your character—such as a hobby, volunteer work, or a challenging life experience—and explicitly tie them to traits Palantir values, like adaptability, intellectual curiosity, or resilience.
In one sentence, state who you are beyond engineering—e.g., 'I'm a lifelong learner who thrives on creative problem-solving and community building.'
Pick stories that reveal your values, such as leading a volunteer project, pursuing a passion like music or sports, or overcoming a personal challenge.
For each experience, explicitly link it to qualities like adaptability, collaboration, or resilience—and how they apply to software engineering.
Explain why these personal traits make you a great fit for Palantir's culture and the challenges of the role, emphasizing ambiguity and teamwork.
Conclude by expressing enthusiasm for bringing your whole self to the team and growing both personally and professionally.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is not a system design interview in the traditional sense.
Start by clarifying the scope and requirements with the interviewer, then outline the high-level architecture and data model before diving into features and edge cases. Structure your answer around user needs, system components, and trade-offs, emphasizing how you handle dietary restrictions and other constraints.
Pro tip: Demonstrate product sense by prioritizing features based on user impact and feasibility, and explicitly discuss how you'd validate assumptions with data or user feedback. Show maturity by acknowledging trade-offs and proposing iterative improvements.
Ask questions to understand the target users, platform (mobile/web), scale, and key constraints. Define the core problem and success metrics.
Identify main entities like User, Restaurant, MenuItem, Review, and DietaryRestriction. Describe their attributes and how they relate (e.g., User has preferences, Restaurant offers MenuItems).
List essential features such as search by location, filter by cuisine/diet, view details, and leave reviews. Walk through primary user journeys.
Discuss how to handle dietary needs (e.g., tagging, filtering, user-generated data), location inaccuracies, sparse data, and scalability. Propose solutions like fallback mechanisms or crowdsourced verification.
Highlight trade-offs in data consistency, latency, and cost. Suggest potential enhancements like personalized recommendations or integration with delivery services.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem and constraints, then walk through a simple example to confirm understanding. Think aloud as you outline a brute-force solution, then optimize it by discussing trade-offs and choosing appropriate data structures. Write clean code while explaining each step, and test with edge cases.
Pro tip: Palantir values engineers who can communicate complex ideas clearly and collaborate effectively. Treat the interviewer as a teammate: ask for their input, explain your reasoning, and show you can adapt based on feedback.
Restate the problem in your own words and ask clarifying questions about input/output, constraints, and edge cases. Confirm assumptions with the interviewer before proceeding.
Walk through a small example to solidify your understanding and identify patterns. Discuss potential edge cases and how they might affect your approach.
Start with a brute-force solution, then analyze its time and space complexity. Propose optimizations, discussing trade-offs and alternative data structures or algorithms.
Write clean, modular code while narrating your thought process. Explain each step, variable choice, and how it aligns with your overall plan.
Run through test cases, including edge cases, and verify correctness. If time permits, discuss potential improvements or follow-up questions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.