Palantir

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Enterprise and forward-deployed AI · International · Loop reviewed 2026-09-26

Guides and a hiring-side account centre the loop on decomposition: turning an ambiguous real problem into a scoped plan. A 2025 candidate report adds a technical round judged on reasoning under ambiguity. Guides also report restrictions on AI assistance; confirm the current team's format and tool rules.

On this page: Interview loop · Questions (27)

Roles covered

• AI Engineering: Forward deployed engineer (FDE), Software engineer (general interview baseline)
• AI Leadership: Deployment strategist

Interview loop

Stage	What happens	Basis
Recruiter call Forward deployed engineer (FDE)	About 30 minutes on motivation and connection to the mission.	† prep guide or compilation Palantir Forward Deployed Engineer (FDE) Interview Guide, Aced (formerly Exponent), retrieved 2026-09-26
Technical screen Forward deployed engineer (FDE)	Live coding or an online assessment with a coding task, a SQL query and an API task.	† prep guide or compilation Palantir Forward Deployed Engineer (FDE) Interview Guide, Aced (formerly Exponent), retrieved 2026-09-26
Onsite Forward deployed engineer (FDE)	Three hour-long rounds drawn from decomposition, coding, learning an unfamiliar system, re-engineering a large codebase and system design.	† prep guide or compilation Palantir Forward Deployed Engineer (FDE) Interview Guide, Aced (formerly Exponent), retrieved 2026-09-26 AI Engineering Interview Questions Company Wise, Outcome School, published 2026-09-19, retrieved 2026-09-26
Hiring manager interview Forward deployed engineer (FDE)	About an hour that revisits weaker areas, ownership and team fit.	† prep guide or compilation Palantir Forward Deployed Engineer (FDE) Interview Guide, Aced (formerly Exponent), retrieved 2026-09-26 AI Engineering Interview Questions Company Wise, Outcome School, published 2026-09-19, retrieved 2026-09-26
Decomposition round Forward deployed engineer (FDE), Deployment strategist	Guides describe 45–60 minutes, the first 10–15 spent on questions: an ambiguous operational problem is turned into a scoped, layered plan; deployment strategists practise prompts such as detecting money laundering or fraudulent tax returns.	† prep guide or compilation The Palantir Decomposition Interview, Explained, FDEInterviews.com, retrieved 2026-09-29 Palantir Deployment Strategist Interview Guide, Aced (formerly Exponent), published 2026-08-20, retrieved 2026-09-29
Decomposition, from the hiring side Forward deployed engineer (FDE)	A former Palantir FDE who led FDE recruiting describes explaining a domain such as insider trading and asking what data the candidate would need, what they would ask the customer and what they would look for.	🗣 candidate report So You Want to Hire a Forward Deployed Engineer, First Round Review, published 2026-02-24, retrieved 2026-09-29
Technical round, candidate report Forward deployed engineer (FDE)	In August 2025, after a conversational recruiter screen that asked why Palantir and why this role, an algorithmic problem judged on how the candidate handled ambiguity, explained trade-offs and debugged in real time.	🗣 candidate report Palantir Forward Deployed Software Engineer Interview Experience - New York, New York, Taro, candidate report, retrieved 2026-09-29
Deployment strategist technical bar Deployment strategist	The guide describes learning an unfamiliar SQL-like language in a live technical round; SQL fluency is recommended preparation.	† prep guide or compilation Palantir Deployment Strategist Interview Guide, Aced (formerly Exponent), published 2026-08-20, retrieved 2026-09-29
Phone interviews and onsite	Everyone starts with one or two phone interviews; if there is a fit, an onsite round follows. The process is structured around engineering competencies, each with a published guide.	✅ confirmed by the company Getting Hired, Palantir Careers, retrieved 2026-09-29
Working inside existing systems	One of the published competencies: changing systems, codebases and infrastructure you did not write, for example restoring a customer's system after an outage without first reading all of its code.	✅ confirmed by the company Working Inside Existing Systems, Palantir Careers, retrieved 2026-09-29
Onsite rounds, candidate report Forward deployed engineer (FDE)	March 2025, offer accepted: an engineer screen on breaking down a problem, then coding (optimising a scheduling system), debugging a broken API service, a learning round that integrates an unfamiliar library before a system design, and a hiring manager round.	🗣 candidate report Palantir Forward Deployed Software Engineer Interview Experience - Denver, Colorado, Taro, candidate report, retrieved 2026-09-29
Learning round with SQL, candidate report Forward deployed engineer (FDE)	November 2025: a coding and decomposition interview, then a virtual onsite with a second decomposition and a learning interview that expected SQL queries without running them, although the candidate had been told no preparation was needed.	🗣 candidate report Palantir Forward Deployed Software Engineer Interview Experience - United States, Taro, candidate report, retrieved 2026-09-29

Coding: unknown. A secondary guide describes coding for FDEs; company or first-hand confirmation is absent here. (Palantir Forward Deployed Engineer (FDE) Interview Guide, Aced (formerly Exponent), retrieved 2026-09-26)

Questions reported for Palantir

RAG and retrieval

• Design retrieval that enforces the source system's access rights, including permission changes and shared caches.
  • System design · Asked at: Databricks †, Microsoft †, Palantir † · ✍ Answer
  • Tests: Whether access checks cover the full path to generated output.
  • A strong answer covers:
    • Propagate identity and enforce authorisation before content reaches the model.
    • Keep permission updates, deletions and cache keys consistent with the policy.
    • Test denied access and revocation paths; citations and logs need the same boundary.
  • Read: Document-level access control (Microsoft Learn)
• A user asks for the count of open orders blocked by supplier issues. Why might document retrieval fail, and what should answer the query?
  • Applied scenario · Asked at: Palantir †
  • Tests: Whether structured aggregation is separated from semantic retrieval.
  • A strong answer covers:
    • Explain why top-k retrieval fails: it returns similar passages, not a complete filtered set, so any count is a sample.
    • Route to the system of record: a governed query over order status and block reason, with the model only translating intent.
    • Pin down what counts as blocked by a supplier, show the query and data timestamp, and test against known counts.
  • Read: Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows (Lei et al., arXiv) · Table Expressions (PostgreSQL Global Development Group)

Agents, tools and protocols

• How would you make an agent's production actions reversible where possible and auditable where reversal is impossible?
  • System design · Asked at: Palantir † · ✍ Answer
  • Tests: Whether compensating actions and durable evidence are planned before execution.
  • A strong answer covers:
    • Classify each tool action before release as reversible, compensable within a window, or irreversible.
    • Prefer native undo such as drafts, soft deletes and delayed sends; register a tested compensation for the rest.
    • Write intent, exact arguments and authoriser to an append-only log before acting; rehearse reconstruction from it.
  • Read: Designing robust and predictable APIs with idempotency (Stripe) · Compensating Transaction pattern (Microsoft Learn)
• Design human approval for consequential agent actions, including how approval remains bound to the exact action being executed.
  • System design · Asked at: OpenAI †, Palantir † · ✍ Answer
  • Tests: Whether approval controls are enforced outside the model.
  • A strong answer covers:
    • Preview the concrete action, target and consequences before approval.
    • Bind approval to validated arguments and recheck authorisation at execution.
    • Record the decision and execution result; expire approval after material changes.
  • Read: Building Effective AI Agents (Anthropic) · Designing robust and predictable APIs with idempotency (Stripe)
• Design an agent that creates and updates work orders in a live ERP without duplicating or silently corrupting business operations.
  • System design · Asked at: Palantir † · ✍ Answer
  • Tests: Whether business invariants survive uncertain model and network behaviour.
  • A strong answer covers:
    • Keep the model on proposals: a typed command validated against ERP business rules before any write.
    • Derive a stable business key for idempotent creation, and update with a version check, never last-write-wins.
    • Reconcile ambiguous outcomes before retrying, log proposal against effect, and test duplicate events and concurrent edits.
  • Read: Designing robust and predictable APIs with idempotency (Stripe)
• Why might an enterprise agent operate on an ontology of business objects and actions instead of raw tables and documents?
  • Knowledge · Asked at: Palantir †
  • Tests: Whether semantic modelling is connected to valid operations and permissions.
  • A strong answer covers:
    • Explain that business objects carry meaning, relationships and identity that raw tables leave implicit.
    • Expose typed actions with preconditions and permissions instead of free-form queries and writes.
    • Weigh the modelling and upkeep cost, and test what happens when a task falls outside the ontology.
  • Read: Writing effective tools for AI agents—using AI agents (Anthropic)

Evaluation and observability

• How would you change evaluation when an LLM workflow moves from suggesting actions to executing them in production?
  • System design · Asked at: Palantir † · ✍ Answer
  • Tests: Whether action consequences change the acceptance criteria.
  • A strong answer covers:
    • Grade outcomes in the environment, not the proposal text: what changed, and was it permitted.
    • Weight errors by consequence and reversibility; irreversible actions need near-zero tolerance or human approval.
    • Test in sandboxed replicas, then use shadow mode and staged rollout with action logs and a kill switch.
  • Read: Demystifying evals for AI agents (Anthropic) · OWASP Top 10 for LLM Applications (OWASP Gen AI Security Project)

AI system design

• Design a pipeline that extracts structured fields from ten million scanned documents and makes uncertain results reviewable.
  • System design · Asked at: Palantir † · ✍ Answer
  • Tests: Whether extraction quality, provenance and scalable processing are planned together.
  • A strong answer covers:
    • Process documents as idempotent jobs, storing each field with page, region, model version and confidence.
    • Route fields with low calibrated confidence or failed validation rules to review, sized to reviewer capacity.
    • Measure per-field accuracy on a stratified audit sample, and feed reviewer corrections back into evaluation.
  • Read: Interpret and improve model accuracy and confidence scores (Microsoft Learn) · Structured outputs (Anthropic)
• Design natural-language querying over a warehouse with thousands of tables, from schema selection to safe query execution.
  • System design · Asked at: Databricks †, Palantir † · ✍ Answer
  • Tests: Whether semantic correctness and query permissions constrain generation.
  • A strong answer covers:
    • Retrieve a small candidate schema from catalog metadata, governed metric definitions and verified example queries.
    • Run generated SQL read-only under the requesting user's own warehouse permissions, with cost, row and time limits.
    • Compare result sets with verified queries on real questions, and measure schema-retrieval recall separately.
  • Read: OWASP Top 10 for LLM Applications (OWASP Gen AI Security Project) · Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows (Lei et al., arXiv)
• Design an LLM gateway with provider routing, failover, caching, rate limits and enforceable spending budgets.
  • System design · Asked at: Palantir †, Perplexity † · ✍ Answer
  • Tests: Whether provider differences and failure handling preserve the service contract.
  • A strong answer covers:
    • Define provider capabilities and normalise only compatible request semantics.
    • Enforce tenant quotas and budgets before dispatch, including retries.
    • Specify failover boundaries for partially streamed responses and side-effecting tool calls.
  • Read: Scaling your API with rate limiters (Stripe) · Designing robust and predictable APIs with idempotency (Stripe)

Practical coding

• Write a function that collects a paginated REST result while handling page size, termination and incomplete responses explicitly.
  • Coding · Asked at: Palantir †
  • Tests: Whether boundary conditions prevent missing or duplicated records.
  • A strong answer covers:
    • Prefer cursor pagination: with offsets, concurrent inserts and deletes shift pages and cause gaps or duplicates.
    • Terminate on an absent next cursor, not a short page, and guard against a repeated cursor looping forever.
    • Validate each page, retry failures with backoff, dedupe by id; test empty results and exact page-size multiples.
  • Read: AIP-158: Pagination (Google)
• Optimise the implementation of a given scheduling system, keeping the code clean.
  • Coding · Asked at: Palantir 🗣
  • Tests: Whether you find the real bottleneck and improve it without making the code harder to read.
  • A strong answer covers:
    • Profile on a realistic workload before changing anything, and state the input sizes that matter.
    • Replace the dominant cost with a better structure, such as a heap or interval index, keeping the interface.
    • Keep existing tests passing, add a benchmark, and report complexity and timings before and after.
  • Read: heapq: Heap queue algorithm (Python Software Foundation) · Site Reliability Engineering (Google)
• Debug a broken API service: trace the logs, find the bottleneck and fix a memory leak.
  • Coding · Asked at: Palantir 🗣
  • Tests: Whether you debug a running service systematically from evidence rather than guesses.
  • A strong answer covers:
    • Reproduce the symptom, then follow one request through the logs to find where time is spent.
    • Test one hypothesis at a time; compare heap snapshots over time to find objects that keep growing.
    • Fix the root cause, such as an unbounded cache or unreleased handles, and prove the fix under load.
  • Read: Site Reliability Engineering (Google) · Working Inside Existing Systems (Palantir Careers)
• Write SQL queries to answer questions about a dataset, without being able to run them.
  • Coding · Asked at: Palantir 🗣
  • Tests: Whether your data-querying fundamentals hold up without tools to check them.
  • A strong answer covers:
    • Restate each question as the result's grain: what one row represents and which rows qualify.
    • Watch for join fan-out, NULL semantics in filters and aggregates, and WHERE versus HAVING.
    • Trace each query by hand on a few sample rows, including ties, NULLs and empty groups.
  • Read: Table Expressions (PostgreSQL Global Development Group)

AI platform and operating model

• How would you support multiple model providers when some deployment environments permit only a subset of models?
  • System design · Senior · Asked at: Palantir † · ✍ Answer
  • Tests: Whether a platform preserves deployment constraints and makes capability gaps visible.
  • A strong answer covers:
    • Make environment policy and data restrictions explicit in model selection.
    • Define a portable interface without pretending all models have identical capabilities.
    • Evaluate allowed fallbacks and fail closed when no compliant route exists.
  • Read: Architect multitenant solutions on Azure (Microsoft Learn) · AI Risk Management Framework (NIST)
• Design a service that lets several teams query a shared dataset without exposing the underlying raw records.
  • System design · Senior · Asked at: Palantir † · ✍ Answer
  • Tests: Whether you define permissible results and enforce access across a shared platform.
  • A strong answer covers:
    • Define permissible results per team and purpose: fields, aggregations and a minimum group size.
    • Enforce policy in the service: caller identity, query rewriting, row and column controls, output checks.
    • Defend against differencing and repeated slicing, log every query, and test with re-identification attempts.
  • Read: Guidelines for Evaluating Differential Privacy Guarantees (SP 800-226) (NIST)

Applied and customer scenarios

• A customer executive wants to cancel an AI pilot because it keeps producing wrong results. What would you do over the next 48 hours?
  • Applied scenario · Senior · Asked at: Palantir † · ✍ Answer
  • Tests: Whether you combine incident triage with a credible customer recovery plan.
  • A strong answer covers:
    • Acknowledge impact and collect concrete failing workflows with the customer.
    • Contain harmful behaviour and separate data, system and expectation problems.
    • Agree on a short recovery experiment and explicit continue-or-stop criteria.
  • Read: Site Reliability Engineering (Google) · Demystifying evals for AI agents (Anthropic)
• A freight railway loses substantial revenue to unexpected locomotive downtime. Turn the problem into an engineering plan.
  • Applied scenario · Senior · Asked at: Palantir †
  • Tests: Whether you connect operational decisions, usable data and intervention value.
  • A strong answer covers:
    • Ask which failure modes cost most and which decision an early warning would change.
    • Check whether sensor and repair logs label failures, then price false alarms against missed breakdowns.
    • Close on a pilot fleet, comparing unplanned downtime and cost with matched locomotives.
  • Read: Rules of Machine Learning (Google for Developers) · Site Reliability Engineering (Google)
• How would you design a system to improve traffic in New York City?
  • System design · Senior · Asked at: Palantir †
  • Tests: Whether you decompose an ambiguous city-scale objective into measurable interventions.
  • A strong answer covers:
    • Ask what improving traffic means and for whom: travel time, safety, emissions, buses or freight.
    • Choose levers the system actually controls, such as signal timing or bus lanes, and their data.
    • Evaluate one corridor against a comparable control, watching for traffic displaced onto nearby streets.
  • Read: User Needs + Defining Success (Google PAIR) · Data and AI Ethics Framework (Government Digital Service)
• Design synchronisation between two systems that hold employee records.
  • System design · Senior · Asked at: Palantir †
  • Tests: Whether you identify record ownership, conflicts and recovery semantics.
  • A strong answer covers:
    • Assign an owner per field, not per record, and a stable identifier linking both systems.
    • Propagate changes idempotently with version ordering, and define which side wins each conflict.
    • Reconcile regularly, replay safely after outages, and route unmatched or deleted records to review.
  • Read: Designing robust and predictable APIs with idempotency (Stripe)
• Design an application for recording species while exploring an unfamiliar environment.
  • System design · Senior · Asked at: Palantir †
  • Tests: Whether you clarify field constraints and support trustworthy data collection.
  • A strong answer covers:
    • Ask about field constraints first: connectivity, battery, gloves, light and who records.
    • Design capture to work offline with location, time and photo, and resolve sync conflicts later.
    • Store the model's suggestion apart from the observer's call, and audit a sample against experts.
  • Read: Build an offline-first app (Android Developers)
• After an introduction to insider trading, explain how you would detect it: what data would you need, what would you ask the customer and what patterns would you look for?
  • Applied scenario · Asked at: Palantir 🗣
  • Tests: Whether you turn a vague, high-stakes problem into a scoped first system that can be tested.
  • A strong answer covers:
    • Ask which instruments and data exist, and what an analyst must decide from an alert.
    • Scope one pattern first: unusual trading by connected accounts before price-moving announcements.
    • Backtest on confirmed past cases, and cap alert volume at what analysts can review.
  • Read: Insider Trading Detection: FINRA’s Vital Role in Ensuring Market Integrity (FINRA)
• Integrate a library you have never used into an existing system within minutes, then design the surrounding system for scale, storage and failures.
  • Applied scenario · Asked at: Palantir 🗣
  • Tests: Whether you learn an unfamiliar tool quickly and carry it into sound design decisions.
  • A strong answer covers:
    • Read the core abstraction and one minimal example, then wrap the library behind a thin interface.
    • Carry what you learned about its limits, state and errors into the scale and storage design.
    • Name how the design fails, plan retries, timeouts and backpressure, and test the integration path.
  • Read: Working Inside Existing Systems (Palantir Careers)

Behavioral and values

• Describe a time you pushed back on a customer's request.
  • Behavioral · Senior · Asked at: Palantir †
  • Tests: Whether you protect the customer's underlying goal while challenging a proposed solution.
  • A strong answer covers:
    • Choose a request where the customer's proposed solution would have hurt their own underlying goal.
    • Show how you uncovered the goal behind the request and offered an alternative that served it better.
    • Close with what the customer decided, how the relationship held, and evidence the goal was met.
  • Read: Communication (GitLab Handbook)
• How do you think about work for defence or intelligence customers, and what would you do if a requested project conflicted with your values?
  • Behavioral · Senior · Asked at: Palantir †
  • Tests: Whether you reason about concrete boundaries and professional responsibility.
  • A strong answer covers:
    • State your actual position on defence and intelligence work and its reasoning, whichever way it points.
    • Draw a concrete boundary — which uses, what oversight, whose decision — rather than a general statement of values.
    • Lay out your steps on conflict: raise it, escalate, leave the project, and when you would quit.
  • Read: Data and AI Ethics Framework (Government Digital Service)
• Why this company, and why the forward deployed role rather than engineering on the core product?
  • Self-presentation · Asked at: Palantir 🗣
  • Tests: Whether your motivation fits customer-facing delivery work, not only the company's name.
  • A strong answer covers:
    • Tie the company choice to specifics you checked, such as its customers and the problems it deploys into.
    • Explain why customer-facing delivery suits you better than core product work, including what you give up.
    • Back the motivation with your own track record of working directly with users and shipping into their environments.
  • Read: Forward Deployed Engineering (Ramp Builders)
• What might keep you from succeeding in this job?
  • Self-presentation · Asked at: Palantir †
  • Tests: Whether you assess your fit for an ambiguous, customer-facing role honestly.
  • A strong answer covers:
    • Name a real risk tied to this role's demands — ambiguity, customer pressure, context switching — not a disguised strength.
    • Pick a risk that matters but is not disqualifying, and explain how it has shown up before.
    • Describe how you mitigate it in practice and what early signal would tell your manager it is happening.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)

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