People talking at the far end of a long wooden meeting table in afternoon light

Forward deployed engineer interview questions

Interview prep

Forward deployed engineer interview questions, each with what it tests, a strong answer outline and the mistake that sinks most candidates. They match the format companies and prep guides describe in public. They are not leaked questions. Companies can also hire forward deployed engineers through us.

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The forward deployed engineer interview process

A forward deployed engineer interview usually has a recruiter screen, a practical coding round, a system design round set inside a customer's environment, an open-ended case and a behavioural round on ownership and customers. AI-focused companies add an LLM build or take-home and ask hard questions about evaluation.

Most loops share a shape. What changes between companies is the weight on each round.

Recruiter screen. Background, motivation, location and travel, and pay expectations.
Technical screen. Practical coding, sometimes run by a third party, or a take-home project.
Coding round. Messy data, an unfamiliar API or a bug in code you did not write. Less about puzzles, more about whether your code would survive a customer's data.
System design. Design inside a customer's cloud, sign-in system and data stack, not a blank-page startup.
Case or decomposition. A vague problem you break down and cut to a first version.
Customer simulation. Role-play: discovery, pushback and delivering bad news.
Behavioural and hiring manager. Ownership, ambiguity, conflict and why this company.

If you are new to the role, start with what a forward deployed engineer is and the job description. The interview tests what is on those pages.

How the interview differs by company

Palantir (Forward Deployed Software Engineer)

Palantir says the number and format of interviews is set for each candidate and role, starting with phone interviews before in-person rounds. Its own advice for open-ended questions is to talk through alternatives and trade-offs, but get a working idea on the table first and build from there.

Palantir describes the engineer it calls a "Delta" as the one who makes many capabilities work for one customer. Expect the interview to test whether you can take one customer's messy problem and turn it into something that works. Prep guides report a recruiter screen, an outside coding screen, then rounds on coding, system design, decomposition and behaviour, with a hiring manager final. The same guide calls decomposition the hardest round, and jumping to a solution in it the most common reason for rejection.

OpenAI (Forward Deployed Engineer)

OpenAI's forward deployed team is new. The Pragmatic Engineer describes its work in phases: early scoping with the customer, checking the work by building evals, then delivery. Prep guides report a recruiter screen, a take-home (a retrieval system, an agent or an eval harness), a walkthrough of that take-home, then rounds with a hiring manager, a second technical interview and a design or case round. The main warning: expect hard questions on how you know an AI system is actually working.

Anthropic (Forward Deployed Engineer, Applied AI)

Anthropic's post described embedding with strategic customers, building production applications on Claude inside customer systems, and delivering MCP servers, sub-agents and agent skills. It asked for time in a technical, customer-facing role, production experience with LLMs including evaluation, and strong Python, and it listed travel. Prep guides report a recruiter screen, a take-home, a hiring manager screen, a timed coding assessment, technical interviews and a mission alignment round. Anthropic's candidate guidance lets you use Claude to polish application materials you drafted yourself, but not in take-homes or live interviews unless they say otherwise.

Google (Forward Deployed Engineer, Cloud and Applied AI)

Aced reports a recruiter screen, a standard Google data structures and algorithms coding round, a system design round on agentic and machine learning systems such as retrieval and vector search, and a behavioural round. The exact loop varies by team and level. If Google is on your list, do not skip classic algorithm practice the way you might for other loops.

What every round is really testing

Strip away the company names and these loops look for the same things. Use them to check your answers.

You anchor on a user and a measure. Who has the problem, and how you would know it got better.
You ship a first version early. A working skeleton beats a perfect plan.
You can work in someone else's environment. Their cloud, their security team, their messy data.
You talk while you think. Prep guides name silence in coding and design rounds as a common failure.
You own outcomes personally. Interviewers screen for "I", not "we".

Practice

Forward deployed engineer interview questions, round by round

Practice questions for each round type. Open a question to see the outline. Practise out loud, with a timer, and with someone playing the customer.

Decomposition and case

A vague, real-world problem you break into users, data, logic and a first version.

Decomposition and case A city wants to cut emergency response times. They have call logs, traffic data and ambulance GPS. Where do you start?
What it tests: Whether you anchor on a user and a measurable outcome before you touch the data.
Strong answer outline
- Ask who the user is: dispatchers, crews, or the city budget office. Pick one for the first version and say why.
- Define the metric. Call received to unit on scene, split into answer time, dispatch time and travel time.
- Use the data to find which segment is slowest before proposing a fix. Travel time and dispatch time need different products.
- Propose a walking skeleton: a dashboard that shows each segment per call over recent months, built quickly.
- Name the next step and the risk: GPS gaps, timestamp clock drift between systems, and privacy rules on call data.
Common mistake: Opening with "I would build a model to predict where emergencies happen" before knowing which part of the response is slow.
Decomposition and case A regional bank bought several smaller banks. Each has its own fraud system with different labels. Scope the first quarter.
What it tests: Scoping judgement and whether you can sequence work so value shows up early.
Strong answer outline
- Clarify the goal: one fraud queue for analysts, one model, or one set of reports for regulators. These are different projects.
- Map the labels across the systems and find where they disagree. That mapping is the real first deliverable.
- Early on: land the feeds in one place with a shared schema and a reviewed label mapping.
- Next: one unified analyst queue so investigators stop switching tools. Measure time per case.
- Then: retrain or recalibrate scoring on unified labels, run it in shadow mode, compare against current alerts.
Common mistake: Promising a single new fraud model in one quarter without accounting for the label cleanup that has to come first.
Decomposition and case A hospital network says patients wait too long for a bed after admission. Break the problem down.
What it tests: Structured breakdown of a physical operations problem into data, decisions and owners.
Strong answer outline
- Trace the flow: admit decision, bed request, bed assigned, bed cleaned, patient moved. Each handoff has an owner.
- Ask which systems hold each timestamp. Often the EHR, a bed board and an environmental services tool.
- Find the biggest delay first. If it is cleaning turnaround, the product is a task queue for cleaners, not a forecasting model.
- First version: a live board of pending moves with time stuck at each step, shared by the bed manager and the cleaning lead.
- State constraints: health data rules, hospital IT approval cycles, and shift change times.
Common mistake: Treating it as a pure prediction problem and skipping the people who actually move patients and clean rooms.
Decomposition and case A logistics company wants an AI agent that reroutes shipments automatically using their ERP, a weather API and input from a large number of warehouse managers. How do you approach it?
What it tests: Whether you can cut an overambitious AI ask down to something safe and shippable.
Strong answer outline
- Separate the decision (reroute or not) from the action (change the order in the ERP). Automating the action is the risky part.
- Start with recommendations a human approves. Log every approval and rejection as labelled data.
- List the inputs the agent needs and which are reliable: ERP order status, carrier capacity, weather alerts.
- Define what "good" means: on-time delivery rate, cost per reroute, number of bad suggestions per week.
- Move to automatic actions only for narrow cases where approval rates are very high, with a kill switch.
Common mistake: Designing a fully autonomous agent with write access to the ERP on day one.
Decomposition and case A retailer with a large store network has frequent stockouts on its top products. You have one week before your first update to the VP. What do you do?
What it tests: Bias to a working first artefact under time pressure.
Strong answer outline
- First: talk to some store managers and a buyer. Find out how they detect a stockout today.
- Next few days: pull sales, inventory and replenishment orders for the top products. Measure where the stockouts actually happen.
- End of the week: build a simple daily list of products at risk per store, using days of cover.
- Show the VP the list, the hit rate against last month, and the data problems you found.
- Ask for one region to trial it with, and a named owner on their side.
Common mistake: Spending the whole week on data plumbing and showing up with no artefact and no finding.
Decomposition and case Halfway through your decomposition, the interviewer adds a constraint: the customer has no cloud account and all data must stay on site. What changes?
What it tests: How you update a plan when the ground shifts, without starting over.
Strong answer outline
- Restate what still holds: the user, the metric and the first version are the same.
- List what changes: hosting, model choice, update process and who maintains the hardware.
- Offer two options with trade-offs, for example a small on-prem cluster versus a single server with a scheduled batch job.
- Pick one for now and say what would make you switch.
Common mistake: Throwing out the whole plan, or ignoring the constraint and carrying on as before.

Customer discovery

Role-play with a stakeholder. Finding the real problem, saying no, delivering bad news.

Customer discovery A customer executive says, "We need AI for our claims process." You have a short call. Run it.
What it tests: Discovery questions that get to a specific workflow, an owner and a number.
Strong answer outline
- Ask what happens to a claim today from intake to payout, and where it waits longest.
- Ask who does that step, how many people, and how many claims a week.
- Ask what a good outcome looks like later this year and how they would measure it.
- Ask what has been tried before and why it stopped.
- Close with a concrete next step: a visit to the team doing the work and access to a sample of real claims.
Common mistake: Pitching features or a demo before you know which step of the process hurts.
Customer discovery Your deployment will slip by several weeks. The customer CTO is on the line. Tell them.
What it tests: Delivering bad news early, plainly, with a plan.
Strong answer outline
- Lead with the news in the first sentence. No preamble.
- Give the cause in a sentence or so, including your own part in it.
- Show what still ships on the original date, if anything.
- Offer options: cut scope to keep the date, or keep scope and move the date. Recommend one.
- Agree on how they will hear progress, for example a short written update on a set schedule.
Common mistake: Burying the slip under status detail, or blaming the customer team for the delay.
Customer discovery A business sponsor wants a feature that would expose sensitive data to users who should not see it. Push back without losing them.
What it tests: Saying no while keeping the relationship and solving the underlying need.
Strong answer outline
- Find out the need behind the request. Usually it is speed, not access to the raw data.
- Explain the risk in their terms: audit findings, regulator exposure, a headline.
- Offer a version that meets the need: aggregated views, masked fields, or an approval flow.
- Bring in the data owner or security lead early so the decision is not yours alone.
Common mistake: Either saying yes to keep the sponsor happy, or a flat no with nothing offered in its place.
Customer discovery Explain to a non-technical VP why your retrieval system cannot promise perfect accuracy, and what you will do about it.
What it tests: Explaining model limits without jargon and turning them into an operating plan.
Strong answer outline
- Compare it to a new hire: right most of the time, sometimes wrong, and you design the process around that.
- Give the measured accuracy on their own test set and what kinds of questions it misses.
- Show the guardrails: citations to source documents, refusal when nothing relevant is found, human review for high-stakes answers.
- Agree a target and a review cadence, and what happens if quality drops.
Common mistake: Hiding behind technical terms, or overpromising and getting caught later.
Customer discovery The operations lead and the IT lead at the same customer want opposite things from your project. What do you do?
What it tests: Navigating customer politics without picking a side blindly.
Strong answer outline
- Meet each one separately and write down what each needs and why.
- Look for the shared goal. Often ops wants speed and IT wants control, and both want the project to survive.
- Propose a design that gives ops a fast path inside guardrails IT signs off on.
- If they still disagree, take it to the executive sponsor with both options written up, not your opinion alone.
Common mistake: Siding with whoever is loudest, or building two half solutions to keep both happy.
Customer discovery A few weeks in, you realise the problem the customer asked you to solve is not the one costing them money. What now?
What it tests: Judgement to change course and the communication to bring the customer along.
Strong answer outline
- Gather evidence first: numbers that show where the cost really sits.
- Share it with your direct contact before any wider meeting, so they are not surprised in front of their boss.
- Propose a small pivot, not a restart. Reuse what you already built where possible.
- Write down the new success measure and get it agreed.
Common mistake: Quietly finishing the original scope because it is what the contract says.

System design for deployments

Design inside a customer environment: their cloud, their identity provider, their data.

System design for deployments Design a private document search and Q&A system for a healthcare customer. It must run in their own cloud account and handle a very large number of documents.
What it tests: Designing under real customer constraints: network boundaries, compliance, scale.
Strong answer outline
- Clarify: who asks questions, how fresh the data must be, which document systems are sources, and what compliance rules apply.
- Ingestion: connectors per source, a queue, parsing and chunking workers, and a job table so failed documents can be retried.
- Storage: a vector index plus a keyword index, with document-level access control copied from the source system.
- Serving: retrieve, rerank, generate with citations, and filter results by the user's permissions before the model sees them.
- Operations: everything inside their network, audit logs on every query, an eval set from real questions, and cost per query tracked.
Common mistake: Ignoring access control, so the system can leak a document to someone who could not open it in the source.
System design for deployments A large company wants to run your platform in their AWS account, with Okta for sign in and Snowflake as the data source. Walk through the deployment.
What it tests: Enterprise integration knowledge: identity, networking, secrets, data access.
Strong answer outline
- Identity: SAML or OIDC with Okta, group to role mapping, and a plan for offboarding.
- Network: private subnets, private connectivity to Snowflake where available, no public endpoints unless approved.
- Data access: a service user with read-only access to agreed schemas, key-pair auth, and secrets in their secrets manager.
- Deployment: infrastructure as code they can review, a staging environment, and a documented upgrade path.
- Hand-off: runbooks, alerts routed to their on-call, and a list of what you own versus what they own.
Common mistake: Designing as if you control the environment. In a customer account, their security team approves every step.
System design for deployments Design a pipeline that pulls many messy retail data sources into one model for demand forecasting.
What it tests: Data integration design: contracts, quality checks and recoverability.
Strong answer outline
- Inventory the sources: format, update frequency, owner and known quality problems.
- Land raw data unchanged first, then transform in versioned steps so you can replay history.
- Add data checks at each step: row counts, null rates, duplicate keys, freshness. Fail loudly.
- Build one clean, documented table per business entity: product, store, sale, inventory.
- Schedule, monitor and alert. Note which sources are likely to change shape without warning.
Common mistake: Jumping to the forecasting model and treating the many sources as a solved detail.
System design for deployments Your software must run at a site with unreliable internet, like a factory or a ship. Design the sync between the site and the central system.
What it tests: Handling partial connectivity, conflicts and recovery.
Strong answer outline
- Make the site work fully offline for its core tasks.
- Store changes locally as an append-only log with timestamps and IDs.
- Sync in both directions when connected, with clear rules for conflicts, for example last write wins per field or a manual review queue.
- Make every sync idempotent so a dropped connection mid-transfer causes no duplicates.
- Show sync status to users so they know what is current.
Common mistake: Assuming a constant connection and calling the central API directly from the site.
System design for deployments You built a custom workflow for one customer. Several more customers want something similar. How do you turn it into something reusable without slowing the first customer down?
What it tests: The FDE to product handoff: finding the general pattern inside custom work.
Strong answer outline
- List what is the same across the customers and what is different.
- Pull the shared parts into a configurable module. Keep the customer-specific logic in configuration or plugins.
- Migrate the first customer last, once the module is proven on a new one.
- Write it up for the product team with usage data, so it can join the core platform.
Common mistake: Copying the first customer's code for each new customer and maintaining a fork for every one.

Coding in an unfamiliar codebase

Practical code, messy data, reading code you did not write, debugging under time.

Coding in an unfamiliar codebase Here is a CSV export from a customer system. Dates come in several formats, some amounts have currency symbols, and some rows are duplicated. Write code to load it into a clean table.
What it tests: Practical data handling, defensive parsing and clear reporting of bad rows.
Strong answer outline
- Look at a sample of rows before writing any code and list every format you see.
- Parse each field with explicit rules. Do not let a library guess date formats silently.
- Decide what counts as a duplicate and say it out loud: same ID, or same ID plus timestamp.
- Send rows that fail parsing to a rejects file with the reason, and count them.
- Write a few tests for the odd cases you found.
Common mistake: Dropping bad rows silently. The customer will find out from a wrong total.
Coding in an unfamiliar codebase Implement a rate limiter for calls to a third-party API that allows a fixed number of requests per minute per customer.
What it tests: Clean code for a common production problem, and awareness of concurrency.
Strong answer outline
- Pick an approach and say why: token bucket or sliding window.
- Write the single-process version first, with a clear interface like allow(customer_id).
- Discuss what changes with many processes: a shared store such as Redis and atomic operations.
- Cover edge cases: clock changes, bursts at the window boundary, and what the caller does when blocked.
Common mistake: Writing a clever version without tests or without explaining the trade-offs as you go.
Coding in an unfamiliar codebase A webhook consumer sometimes processes the same event twice and sometimes misses events when the downstream API is down. Fix it.
What it tests: Idempotency, retries with backoff and failure handling.
Strong answer outline
- Store each event ID when processed and skip repeats. That makes it idempotent.
- Acknowledge the webhook fast, put the event on a queue, and process it in a worker.
- Retry failed downstream calls with exponential backoff and jitter, up to a limit.
- Send events that keep failing to a dead letter queue with an alert.
- Add a way to replay events from the dead letter queue after a fix.
Common mistake: Adding a retry loop inside the webhook handler, which makes timeouts and duplicates worse.
Coding in an unfamiliar codebase You get access to a repository you have never seen. A customer reports that a report shows wrong totals. You have under an hour. Find the bug.
What it tests: Reading unfamiliar code fast and narrating your search.
Strong answer outline
- Reproduce it first with a known input and the expected total.
- Find the entry point for the report and trace the data path, reading only what is on that path.
- Use search, logging and a debugger rather than reading every file.
- Form a hypothesis, test it, and say it out loud. Common causes: a join that duplicates rows, timezone boundaries, rounding.
- Fix with a test that fails before and passes after.
Common mistake: Reading the codebase top to bottom, or going silent for long stretches.
Coding in an unfamiliar codebase Write a SQL query that returns each customer's most recent order and the number of days since their previous order.
What it tests: SQL fluency, especially window functions, which come up constantly in customer data work.
Strong answer outline
- Use ROW_NUMBER() partitioned by customer and ordered by order date descending to find the latest order.
- Use LAG() over the same partition, ordered ascending, to get the previous order date.
- Handle customers with one order: the gap should be null, not zero.
- Mention ties on the same timestamp and how you would break them.
Common mistake: Using a correlated subquery per row and not noticing it will be slow on a large table.
Coding in an unfamiliar codebase Here are the docs for an API you have never used. Build a small script that pulls all records, handling pagination and auth.
What it tests: Learning a new tool on the spot, which is what Palantir-style learning rounds are described as testing.
Strong answer outline
- Read the auth and pagination sections first. Ask clarifying questions if the docs are vague.
- Make one request by hand to see the real response shape.
- Loop through pages until there is no next cursor, respecting rate limits.
- Handle errors: expired tokens, rate limit responses, partial pages.
- Save results somewhere you can rerun from, so a failure halfway does not restart everything.
Common mistake: Guessing at the API shape instead of reading the docs and testing one call.

LLM deployment

Retrieval, agents, evals, cost and failure modes for models running against real users.

LLM deployment How do you know your LLM feature is working in production?
What it tests: Evaluation discipline. Prep guides report this as a heavy focus at OpenAI.
Strong answer outline
- Build a test set from real user inputs with expected outputs or grading rules, agreed with the customer.
- Use automated checks where possible: exact match, schema validity, citation present, model-graded rubrics checked against human grades.
- Run the test set on every prompt, model or retrieval change before it ships.
- In production, log inputs and outputs, sample them for human review, and track user signals like edits and thumbs down.
- Report quality as numbers the customer cares about, not just an accuracy score.
Common mistake: Answering "we tried it and it looked good." Vibe checks do not survive a model update.
LLM deployment Users say your retrieval assistant gives confident wrong answers. How do you debug it?
What it tests: Separating retrieval failures from generation failures.
Strong answer outline
- Collect failing examples and label them: right document not retrieved, retrieved but ignored, or the source itself is wrong.
- For retrieval misses, check chunking, metadata filters, the embedding model and whether keyword search would have found it.
- For generation errors, tighten instructions, require citations, and allow "I do not know" when context is weak.
- Add the failing examples to the eval set so the fix stays fixed.
Common mistake: Swapping to a bigger model before finding out whether the right document was even retrieved.
LLM deployment Design an agent that can read and update records in a customer's CRM. What guardrails do you put in place?
What it tests: Safe tool design for agents with write access.
Strong answer outline
- Give the agent narrow tools, like update_contact_phone, not a raw API or SQL access.
- Scope credentials to the least access needed, per user where possible.
- Require confirmation for writes, at least until approval rates show it is safe.
- Log every tool call with inputs, outputs and the user who triggered it.
- Test with adversarial inputs, including instructions hidden inside CRM notes.
Common mistake: Handing the model a general-purpose API key and trusting the system prompt to keep it in line.
LLM deployment Your LLM feature works, but the monthly bill is far above what the customer budgeted. What do you do?
What it tests: Cost awareness and practical levers.
Strong answer outline
- Measure where the tokens go: which calls, which prompts, how much is repeated context.
- Cache repeated prompts and responses where the provider supports it.
- Route easy requests to a smaller model and keep the large one for hard cases. Check quality on the eval set.
- Trim context: fewer, better retrieved chunks and shorter system prompts.
- Batch work that does not need a real-time answer.
Common mistake: Cutting cost by switching models without rerunning the eval set.
LLM deployment A bank wants to use an LLM on customer emails that contain personal data. What do you need to settle before building?
What it tests: Data handling, vendor terms and compliance awareness.
Strong answer outline
- Where the model runs and where data is stored, and whether that meets the bank's data residency rules.
- The provider's data retention and training terms, confirmed in writing.
- Whether personal data can be masked before the model sees it, and whether the task still works masked.
- Access controls, audit logging, and who reviews outputs before they reach a customer.
- Sign-off from the bank's security, privacy and model risk teams.
Common mistake: Building a prototype on real customer data before the legal and security questions are answered.
LLM deployment You need an LLM to pull many fields from scanned invoices into the customer's ERP. How do you make it reliable?
What it tests: Structured output, validation and human-in-the-loop design.
Strong answer outline
- Define a strict schema and use the provider's structured output feature.
- Validate every field: types, totals that add up, vendor IDs that exist in the ERP.
- Send low-confidence or failed validations to a human review queue.
- Measure field-level accuracy on a labelled set of real invoices, per vendor format.
- Write to the ERP through its API with idempotency keys, never by editing its database directly.
Common mistake: Trusting the JSON because it parsed. Valid JSON can still hold the wrong number.

Behavioural

Ownership, ambiguity, travel, conflict. They want "I", not "we".

Behavioural Tell me about a time you owned a project end to end, from a vague request to production.
What it tests: Individual ownership. Prep guides say interviewers screen hard for "I", not "we".
Strong answer outline
- Pick a real story with a vague start and a shipped end.
- Say what you personally did at each stage: scoping, building, deploying, supporting.
- Give a result with a number: users, hours saved, revenue, errors cut.
- Say what you would do differently.
- Keep it short, then let them dig in.
Common mistake: Saying "we" the whole way through so the interviewer cannot tell what you did.
Behavioural Describe a time you had to make progress without clear requirements.
What it tests: Comfort with ambiguity, which every public FDE job description lists.
Strong answer outline
- Explain what was unclear and why waiting was not an option.
- Show how you created clarity: a prototype, a written proposal, a quick user test.
- Show how you checked you were going the right way.
- Give the outcome and what you learned about working without a spec.
Common mistake: Telling a story where you waited for someone else to define the problem.
Behavioural Tell me about a time you disagreed with a customer or a senior stakeholder.
What it tests: Holding a position with evidence while keeping trust.
Strong answer outline
- Set up the disagreement fairly. Show you understood their side.
- Explain what evidence you brought.
- Say how it was resolved, including if you were wrong.
- Show the relationship afterward.
Common mistake: A story where you were simply right and the other person was simply wrong.
Behavioural Why do you want to be a forward deployed engineer and not a product software engineer?
What it tests: Whether you understand the job, including travel and customer pressure.
Strong answer outline
- Point to something you have done that looked like the job: building for a specific user, on site, under a deadline.
- Show you know the downsides: travel, context switching, less deep ownership of one codebase.
- Tie it to the specific company: their customers, their product, a deployment they have written about.
Common mistake: A generic answer about loving AI and impact that could be said to any company.
Behavioural Tell me about a deployment that went wrong.
What it tests: Honesty, accountability and what you changed afterward.
Strong answer outline
- Describe what broke and the impact on the customer, plainly.
- Own your part without excuses.
- Explain how you fixed it in the moment and how you told the customer.
- Show the process change that stopped it happening again.
Common mistake: Picking a fake failure, or one that was entirely someone else's fault.
Behavioural You are deployed with two customers and both have urgent issues on the same day. How do you decide?
What it tests: Prioritisation and communication under load.
Strong answer outline
- Assess impact: which one blocks a live operation or revenue, and which can wait hours.
- Tell both customers what you are doing and when they will hear from you next.
- Pull in help from your team rather than trying to do both alone.
- Afterward, look at why both broke at once and whether something structural needs fixing.
Common mistake: Working on whichever customer shouts loudest and going silent on the other.

A prep plan, round by round

This follows the structure of the plan in the Harvard and Aced guide, cut to what matters most. Work through it in order.

Know the companies. Read what your target companies publish about their forward deployed work. Write your answer to why this job and why this company.
Practical coding. A CSV cleaner, a rate limiter, a webhook consumer with retries and a paginated API client. Add SQL window functions.
System design inside a customer's environment. Single sign-on, private networking, a data warehouse source and audit logs.
Timed decomposition practice. Grade yourself. Check that you asked clarifying questions, named your assumptions, cut to a first version and listed what could fail.
Stories and role-play. A set of short ownership stories told with "I", and customer role-plays with a friend.
Full mock loops. Run the whole loop, then fix your weakest round.

If you want the skills before the interview, see the roadmap. To know what an offer should look like, see salary by company, and browse roles on companies' own careers sites.

Companies can hire forward deployed engineers through us. Send your requirement, and we send a shortlist of people who fit. Engineers can apply once with a CV. Talent never pays.

Sources

Palantir Careers, Getting hired
Palantir Careers, Navigating open-ended questions
Palantir Blog, Dev versus Delta: Demystifying engineering roles at Palantir
The Pragmatic Engineer, What are Forward Deployed Engineers, and why are they so in demand?
Harvard FAS Mignone Center for Career Success, forward deployed engineer interview guide Written by Exponent, now Aced. Loop details are reported by candidates, not confirmed by the companies.
Aced, forward deployed engineer interview guide
Aced, Google forward deployed engineer interview guide
Anthropic, Forward Deployed Engineer, Applied AI (job post via Built In) The post has since been removed.
Anthropic, Guidance on candidates' AI usage

FAQ

Questions about the interview

Are these real questions from Palantir, OpenAI or Anthropic?

No. They are representative questions written to match the round formats those companies and public prep guides describe. We do not publish leaked questions. If you practise the format, the exact wording matters much less.

Do forward deployed engineer interviews include algorithm puzzles?

Sometimes. Prep guides describe Palantir and OpenAI coding rounds as practical rather than puzzle-heavy, while Google loops are reported to keep a standard data structures and algorithms round. Prepare for both: practical data and API work first, then a refresh of core algorithms.

What is a decomposition interview?

An open-ended round, most associated with Palantir, where you get a vague real-world problem and break it into users, data, logic and a first version you could ship. It tests structured thinking under ambiguity, not algorithm knowledge.

Can I use AI tools during the interview?

Check each company. Anthropic publishes candidate guidance: you may use Claude to refine application materials you drafted yourself, but not in take-home assessments or live interviews unless they say otherwise. Assume live rounds are AI-free unless told otherwise.

How long does the interview process take?

Prep guides report that it takes a few weeks from the first call to a decision at Palantir and OpenAI. Timelines vary by level and location.

How should I prepare in the last week?

Run full mock loops with a friend playing the customer, rehearse short ownership stories that use "I", and read what the company has published about its own deployments so your answer on why you want the job is specific.