Forward Deployed Engineer Interview: The Role and Case Round

What forward deployed engineers do at Palantir, OpenAI and Anthropic, what the interview tests, and a worked customer-problem round to practise.

A forward deployed engineer interview checks two things at once: whether you can write solid code, and whether you can take a vague customer problem, break it into something buildable and explain your choices to people who are not engineers. Palantir, which says it pioneered the role, publishes the outline of its process, which starts with one or two phone interviews that include a coding question for engineering candidates. We found no FDE-specific interview loop published by OpenAI or Anthropic, so their job postings are the best evidence, and the postings we reviewed ask for production coding, scoping under ambiguity, customer discovery and frequent travel. This page is part of our guide to what AI engineer interviews focus on, which covers the wider family of roles.

What a forward deployed engineer actually does

The clearest description is still Palantir's own. In an April 2019 post on its engineering blog, Palantir explained that its Software Engineers ("Devs") build its platforms, while its Forward Deployed Software Engineers ("Deltas") deploy and customise those platforms for one customer and measure success by the impact on that customer's goal. The post sums up the split neatly: a Dev's focus is "one capability, many customers", while a Delta's is "one customer, many capabilities". Palantir's current London posting adds that FDEs own projects from the first customer conversation to shipping and should expect 25–50% travel.

The AI labs use the title for the same shape of work. OpenAI's San Francisco posting says FDEs own deployments of frontier models with strategic customers, from discovery and scoping through build and production rollout, with travel of up to 50%. Anthropic's London posting describes FDEs who build production applications with Claude inside customer systems, deliver artifacts such as MCP servers, sub-agents and agent skills, and travel 25–50% of the time. In every version, the FDE owns the delivery of customer outcomes and writes the code that delivers them, so it is reasonable to expect the interview to test scoping alongside algorithms.

How it differs from software, solutions and consulting roles

A product software engineer builds one platform that many customers use, while an FDE uses that platform to solve one customer's problem, which is the Dev and Delta split in Palantir's post. Both write production code, but only the FDE is also judged on whether one customer's outcome actually moved.

A solutions engineer or pre-sales architect focuses on advising customers rather than building production applications inside their systems. Anthropic's Applied AI Architect posting describes a pre-sales role that partners with account executives, acts as the customer's technical advisor and travels "occasionally", while the FDE posting at the same company builds production applications inside customer systems and travels frequently. The pre-sales posting puts more weight on explaining and advising, whereas the FDE posting puts more weight on building and shipping inside customer systems.

A consultant delivers analysis, and an FDE delivers running software. A Delta quoted in Palantir's post said consultants generally produce a one-time analysis or recommendation, whereas Deltas build a long-term solution with the customer, and a colleague added that the role "requires a degree of technical experience that is rare to see in consulting work". If your background is consulting, the coding rounds are where you prove that difference.

What the companies publish about the interview

Palantir publishes the most. Its Getting Hired page says the number and format of interviews are personalised to your skills and the role, that everyone starts with one or two phone interviews, and that candidates who fit are invited onsite. Its phone interview guide says those calls typically last 20 to 45 minutes and that every engineering candidate gets a coding question. Its guide to navigating open-ended questions is the most useful for FDE candidates, since it asks you to weigh alternatives but still reach a concrete approach: "Deliver a functioning idea first, then expand it afterwards." For round-by-round detail, see our breakdown of Palantir's interview process.

Palantir's own pages do not name the individual onsite rounds. A guide from Aced (formerly Exponent), which says it is created from recent first-hand insights shared by interviewers and candidates, describes onsite rounds drawn from a pool that includes decomposition, learning, coding, re-engineering and system design, with decomposition as a 60-minute round where you break a vague real-world challenge into parts with little or no code. Treat that as reported experience rather than policy.

OpenAI's interview guide covers all roles rather than FDEs specifically. It describes a skills-based assessment whose format varies by team, such as pair coding or a take-home project, then final interviews that typically run 4–6 hours with 4–6 people. For engineering interviews, it says it generally looks for well-designed solutions, high-quality code, optimal performance and good test coverage.

We found no interview steps published by Anthropic for the role, but its FDE application form is revealing. It asks whether you have built or deployed an LLM application in an enterprise production environment, and whether you did so while embedded onsite with an enterprise customer, which shows the kind of experience Anthropic is asking about.

What the job descriptions say you will be tested on

Where no loop is published, the postings work as the syllabus, and they line up closely:

• Coding is the baseline: every posting asks for strong programming skills, and our Palantir coding questions and OpenAI coding questions pages show what to drill.
• Problem decomposition is the distinctive part: a Delta in Palantir's post described "technical decomp" as breaking a business problem down to the code that solves it, and OpenAI's healthcare FDE posting asks you to frame ambiguous problems and turn workflows into technical requirements and measurable outcomes.
• System design is likely to involve messy data: Palantir's posting mentions data "at a scale that breaks assumptions", and OpenAI's healthcare posting lists health records, claims systems and protected health information, so expect to discuss data quality, integration and permissions alongside throughput.
• Stakeholder communication runs through everything: Anthropic asks for the skills to run discovery with customers and explain technical ideas to diverse stakeholders, and Palantir wants FDEs to own relationships from frontline users to executives.
• Ambiguity and travel come up directly: Palantir asks for people who decide with incomplete information, and all three postings state the travel commitment up front, so have an honest answer ready.

An illustrative decomposition round: reducing clinic no-shows

The example below is illustrative. We wrote it to show the shape of a strong answer, and it is not a question reported from any company.

The prompt is short on purpose: "A hospital's outpatient clinics want to reduce missed appointments using their scheduling data. How would you approach it?" A weak answer starts with a model, something like "I would train a classifier on patient history", and never asks who will act on the prediction or what they will do differently. A strong answer spends the first few minutes on questions that change the design.

The candidate first clarifies the user and the goal, asking who feels the problem today, what "reduce" means in numbers, and what the clinics already do, such as reminder calls. They then ask about the data: whether each record carries booking date, appointment date, clinic and outcome, whether late cancellations are logged differently from no-shows, and who may see patient-level records, which matters because health data is regulated.

Next, they split the problem into four parts and say them out loud: a clean history of appointments and outcomes, a way to rank tomorrow's appointments by no-show risk, an action staff can take on that ranking, and a way to measure whether it worked. The first version is deliberately small, a daily list per clinic ranked by a simple rule such as the patient's past no-shows and how far ahead the slot was booked, which staff use to decide whom to call first, with the no-show rate tracked in a few pilot clinics.

Only then does the candidate say a trained model can replace the rule once the pilot has produced reliable outcome data. When the interviewer pushes back, perhaps saying the clinic director wants an AI model next month, the strong candidate explains why the simple version ships sooner and creates the labelled data a model would need, which is Palantir's "functioning idea first" advice applied to a customer.

How to tell a customer-facing story in the behavioral round

Palantir's Getting Hired page says onsite interviewers ask how you have executed, challenged yourself, motivated others and made judgments, and where you have failed. For an FDE role, the strongest stories are small versions of the job: a request that hid a different problem, a user who pushed back on what you built, or a deadline that forced you to ship something imperfect and improve it later.

Name the customer and their request, explain how you found the real problem, describe the smaller first version you shipped, and show how you handled pushback. Finish with what changed for the people using it rather than "we launched it", because that outcome is what an FDE is hired to own. Without a client-facing title, internal customers such as an operations team are fair material if you were the one talking to them. Our behavioral interview guide covers how to shape these stories.

A short preparation plan

Work through these steps in order, since each one builds material for the next:

• Read the posting line by line and write down the project or story that proves each requirement, because the posting is the closest thing to a published syllabus.
• Practise coding problems in the language the posting names, talking through your reasoning and starting with a simple working version before you optimise.
• Work through three or four open-ended prompts out loud, following the same path each time: user, goal, data, first version, measurement and next steps.
• Fix a small bug in an unfamiliar open-source project, which is the exercise Palantir suggests for its working-inside-existing-systems competency.
• Prepare four customer stories, and for AI-lab roles, build one small LLM application with a basic evaluation, since OpenAI and Anthropic both ask for production LLM experience.

Practise the customer conversation out loud with Verve AI

The decomposition round is a live conversation, so knowing the structure on paper is not the same as producing it while an interviewer interrupts. In Verve AI's AI Mock Interview, an AI recruiter asks you questions one at a time and you respond in real time, with your answers transcribed, choosing from question banks such as Software Engineering or Behavioral, or the Custom Questions option. When you finish, you get "a detailed performance report with personalized feedback and scoring", which you can read against the structure above to check whether you scoped the problem before solving it. Check each company's rules before the real thing: OpenAI's interview guide says "Expectations for AI and other tools vary by interview: some formats intentionally allow them, while others are designed to assess your independent problem-solving without AI tools", and Anthropic's candidate guidance says of live interviews, "This is all you–no AI assistance unless we indicate otherwise", so practise beforehand and answer on your own on the day.

FAQ

What does a forward deployed engineer do?

A forward deployed engineer works closely with customers to build software that solves their problems, usually on top of the employer's platform or models. Palantir says it pioneered the role and describes the Forward Deployed Software Engineer's focus as "one customer, many capabilities". OpenAI and Anthropic use the title for engineers who build production AI systems inside customer environments, and the postings we reviewed state travel of up to 50%.

Does a forward deployed engineer interview include coding?

Palantir says every engineering candidate gets a coding question in the phone interview, and the OpenAI and Anthropic postings both ask for strong programming skills, although neither company publishes an FDE-specific interview loop. The difference from a standard software loop is that Palantir publishes guidance on open-ended problems and candidate reports describe a decomposition round, so expect scoping to matter at least as much as the code.

What is a decomposition interview?

It is the name candidates use for an open-ended round in which you break a vague real-world problem into parts you could build. Palantir's own guide calls the skill navigating open-ended questions and asks candidates to reach a concrete approach. A strong answer clarifies the user, goal and data before proposing a small first version and a way to measure it, as in the clinic example above.

Sources

• Palantir Devs vs Deltas, "one customer, many capabilities", consultant comparison, "technical decomp" (April 8, 2019): https://blog.palantir.com/dev-versus-delta-demystifying-engineering-roles-at-palantir-ad44c2a6e87
• Palantir FDSE London posting (end-to-end ownership, 25–50% travel, data "at a scale that breaks assumptions", stakeholder relationships, incomplete information): https://jobs.lever.co/palantir/5168e8fd-fec1-4fea-b7a1-81bdaea65850
• Palantir Getting Hired (personalised process, one or two phone interviews, onsite, behavioral topics, competency guides): https://www.palantir.com/careers/getting-hired/
• Palantir phone interview guide (20 to 45 minutes, coding question for every engineer, start simple then expand): https://www.palantir.com/careers/getting-hired/the-phone-interview/
• Palantir open-ended questions guide ("Deliver a functioning idea first, then expand it afterwards"): https://www.palantir.com/careers/getting-hired/navigating-open-ended-questions/
• Palantir working inside existing systems guide (fix a bug in an open-source project): https://www.palantir.com/careers/getting-hired/working-inside-existing-systems/
• Candidate-reported Palantir FDE rounds (decomposition, learning, coding, re-engineering, system design; decomposition 60 minutes, little or no code): https://www.aced.io/guides/palantir-forward-deployed-engineer-interview
• OpenAI FDE San Francisco posting (end-to-end deployments, discovery to rollout, up to 50% travel, production code, LLM systems): https://openai.com/careers/forward-deployed-engineer-(fde)-sf-san-francisco/
• OpenAI FDE Healthcare NYC posting (ambiguous problems, workflows into requirements and outcomes, health records, claims systems, PHI): https://openai.com/careers/forward-deployed-engineer-(fde)-healthcare-nyc-new-york-city/
• OpenAI interview guide (assessment formats, 4–6 hours with 4–6 people, engineering criteria, AI tools wording): https://openai.com/interview-guide/
• Anthropic FDE London posting (customer systems, MCP servers, sub-agents, agent skills, 25–50% travel, discovery and communication, application questions): https://job-boards.greenhouse.io/anthropic/jobs/5423029008
• Anthropic Applied AI Architect posting (pre-sales, partners with account executives, technical advisor, travels occasionally): https://job-boards.greenhouse.io/anthropic/jobs/5383335008
• Anthropic candidate AI guidance (live interviews wording): https://www.anthropic.com/candidate-ai-guidance
• Verve AI Mock Interview (AI recruiter, question banks, Custom Questions, report wording): https://docs.vervecopilot.com/features/mock-interview