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OpenAI Forward Deployed Engineer (FDE) Interview Guide

Updated by OpenAI candidates

The OpenAI forward deployed engineer interview tests your engineering depth and customer judgment in the same exercises, a pairing most big tech engineering loops keep separate. The take-home in many OpenAI FDE loops combines both skills in one assignment, asking you to build a working system for a customer use case, submit the code and a running app, then defend the customer decisions behind it. Later rounds test the same skills, each answering whether you can build the solution and whether an OpenAI hiring manager can trust you in front of the customer.

This guide breaks down each stage of the OpenAI forward deployed engineer interview, what interviewers look for, and how to prepare with example questions, actionable tips, and resources.

OpenAI forward deployed engineer interview process

The OpenAI FDE interview process consists of a recruiter screen, an early technical stage that varies by team, and a virtual onsite. The early stage can be a take-home case study with a separate AI-enabled coding screen, back-to-back technical screens for coding and system design, or a single technical phone screen built around a project you led.

OpenAI’s final round typically includes 4 to 6 interviews across 1 to 2 days. Prepare to discuss your take-home and your strongest project with more than one interviewer.

Here's an example of what the interview process can look like:

  • Recruiter screen: 30-minute call covering your background, motivation for forward deployed work, and hands-on experience building with LLMs
  • Take-home case study: Roughly a week to submit working code, a running app, and a recorded walkthrough, followed by a live discussion with a member of the team
  • AI-enabled coding screen: Multi-step coding challenge at an easy difficulty level, scheduled alongside the take-home
  • Coding interview: Production-style build where you extend a working solution as the interviewer adds requirements
  • System design interview: Production system design with a focus on LLM deployment and agentic architecture
  • Project deep dive: Present and defend a complex system you built, sometimes as the early technical phone screen
  • Behavioral interview: Motivation and AI fluency, then conflict, ownership, and customer-facing work

Recruiter screen

The OpenAI FDE recruiter screen is a 30-minute call that tests your motivation for forward deployed work and your hands-on experience building with LLMs. Recruiters often reach out directly, and the conversation centers on why you want forward deployed work in particular.

Prepare a specific answer for why forward deployed work fits your background, separate from your answer for why you want to join OpenAI. Expect questions on your production experience with LLMs, including whether you’ve used the OpenAI API, and be ready to name a concrete build and what you shipped with it.

Interviewers look for:

  • Motivation specificity: Whether you can explain why forward deployed engineering fits your background and goals
  • AI fluency: A clear perspective on where the technology is heading and how customers will use it
  • Hands-on LLM experience: Whether you’ve built and deployed real systems with LLMs, including OpenAI’s API, and can describe them clearly
  • Role alignment: Whether your background fits embedded, customer-facing technical delivery
  • Communication: How clearly you summarize complex work for a non-technical listener

Sample questions

Here are some real interview questions reported by candidates:

  • Walk me through your background and why this role fits.
  • Why forward deployed engineering specifically, and why now?
  • Have you used the OpenAI API before, and what did you build with it?

Take-home case study

OpenAI’s forward deployed engineer take-home case study asks you to design and ship a working system for a customer use case, then defend your decisions in a live discussion with a member of the team. Expect about a week to submit working code, a running app, and a recorded walkthrough, with a slide deck optional.

The OpenAI FDE take-home varies by team, and not every loop includes one. Confirm the deliverables with your recruiter before you start. The build may take only a few hours of the week, and some teams may assign a written design doc with no code.

Treat the submission as a real customer deliverable: a recent OpenAI FDE candidate described the standard as a clean deployment that shows “you’re not just shipping slop.” The team reviews your submission over about a week and uses it to decide whether you move to the onsite.

Your take-home solution may come up again at the onsite, with engineers and the hiring manager each asking about it from their own perspective. Prepare to explain the same build at the level of technical detail each audience needs.

Interviewers look for:

  • Customer framing: Whether your design starts from what the customer needs and the use case behind it
  • Build quality: Concise code and a clean, working deployment you'd hand to a customer
  • Adaptability: How you'd adjust the system when the customer's needs change
  • Explanation: How clearly you walk through your solution and justify each decision in plain language

Recently asked questions

Here are real, recent interview questions reported by candidates:

  • Build a semantic search system over Amazon product data that ChatGPT can query, then walk through your design.
  • How are you thinking about the use case, and how specific should the search behavior be for different customer intents?
  • Where did you decide to take one path, and where did you decide against another?
  • How would you change the system if the customer's needs shifted?

Coding interview

The OpenAI FDE coding interview tests whether you can reach a correct, working solution and reason about its performance. OpenAI runs an early coding screen and a longer onsite build.

Some OpenAI coding interviews allow AI assistance and others prohibit it, and your prep materials state the rule for each round. Practice with and without AI assistance until you know which format you’ll get.

The early OpenAI FDE coding screen can be a multi-step challenge at an easy difficulty level in a Codex-enabled environment, with follow-ups on time complexity and memory efficiency. Practice stating runtime and memory tradeoffs out loud as you code, and be ready to explain every line an AI tool writes for you.

At the onsite, the OpenAI FDE coding interview can be a 60-minute production-style build, sometimes split into sequential parts. Expect the interviewer to add requirements once your first version works, and build in stages you can extend.

Interviewers look for:

  • Correctness first: Whether you reach a working solution before optimizing
  • Complexity reasoning: How clearly you explain time complexity and memory efficiency
  • Edge-case handling: How thoroughly you reason about inputs that break a naive approach
  • Code quality: Clean structure, sensible naming, and readable abstractions
  • AI tool judgment: Whether you can reason through and verify code you write with AI assistance
  • Language fluency: Command of internals like iterators, generators, async, and concurrency

Sample questions

Here are some real questions reported by candidates in OpenAI engineering interviews:

  • Solve a multi-step delivery-rate calculation, then explain its time complexity and memory efficiency.
  • Implement a credit management system where credits have different expiration rules and usage requirements.
  • Given key/value pairs and helper methods to convert strings to bytes, store and retrieve that state efficiently.
  • Refactor a block of deeply nested, hard-to-read code so it supports new requirements while keeping existing tests passing.
  • Simulate an infection spreading across a 2D grid, passing test cases for each stage before moving to the next.

System design interview

OpenAI's FDE system design interview tests whether you can design reliable production systems for a customer, including how you'd architect a model-backed system on that customer's data. Expect standard infrastructure prompts alongside questions on retrieval, evaluation, and the tradeoffs of building on a model.

Some OpenAI FDE loops run this stage as a solution design round built around an open-ended customer scenario. Start by asking what the customer needs before you propose an architecture.

OpenAI FDE interviewers may look for agentic architectures, where the model handles more of the reasoning and orchestration with fewer hand-coded steps. OpenAI’s published guidance also favors the simplest design that works and keeps deterministic logic where an agent adds nothing.

For each design, be ready to explain which steps you’d hand to the model, which you’d keep deterministic, and how evals confirm the system works once it’s live.

Interviewers look for:

  • Production thinking: Whether your design accounts for retries, idempotency, and failure recovery
  • Scale reasoning: How your architecture performs when usage grows 100x or 1000x
  • LLM tradeoffs: Your judgment on retrieval, fine-tuning, and prompt design for a given use case
  • Agentic design judgment: How you decide which steps to hand to the model and which to keep deterministic
  • Evaluation design: How you'd measure whether a deployed AI system performs correctly
  • Scoping discipline: How you turn a vague customer goal into a concrete technical plan

Sample questions

Here are some example questions to prepare for:

  • Design the retrieval layer for a customer's private knowledge base, and justify your chunking and indexing choices.
  • Design an end-to-end batching system for LLM queries and reason about latency and cost.
  • Design the OpenAI Playground, covering both the frontend and the API surface.
  • What evaluation would you build to catch failures before a customer sees them?
  • What questions would you ask the customer before designing anything?
  • Walk me through how you’d diagnose high latency in an LLM inference pipeline.
  • A customer's traffic spikes 10x overnight. Walk through how you keep the system within latency and cost targets.

Project deep dive

The OpenAI forward deployed engineer project discussion asks you to present a complex system you built and defend each decision you made. Some loops use it as the early technical phone screen, paired with customer-oriented questions.

Choose a project that shows backend ownership, and be ready to explain the services you built and the decisions behind them. If you've built with agentic architectures, lead with that project and explain where the model makes decisions.

Interviewers follow up beyond your prepared summary on what you owned, why you chose each approach, and how the system would scale. Prepare material on the parts of the project that didn't go smoothly as well as the parts that did.

Interviewers look for:

  • Ownership depth: Whether you can show what you personally built and decided
  • Backend expertise: How well you explain the services, data, and infrastructure behind the system you built
  • Design justification: Your reasoning behind storage, model, and architecture choices
  • Scale awareness: How the system performs, or would perform, under heavy load
  • Evaluation rigor: How you measured quality and validated that your system worked
  • Communication under follow-up: How clearly you respond when questioned beyond your prepared summary

Sample questions

Here are some real interview questions reported by candidates:

Behavioral interview

OpenAI's FDE behavioral interview opens with a motivation and AI fluency conversation, then moves to standard questions on conflict, ownership, and cross-functional work. Prepare stories with enough detail for follow-ups, where most of the evaluation happens.

OpenAI FDE interviewers also judge whether your answers sound friendly, patient, and action-oriented when you explain technical work to a customer. Practice softening the directness you’d use with engineers when you explain the same work to customers and internal stakeholders, while keeping the technical content precise.

Expect follow-ups on exactly how you handled difficult conversations with senior stakeholders, such as pushing back on an unrealistic timeline. Prepare the specific words you used and how the stakeholder responded.

A recent OpenAI FDE candidate summed up what the hiring manager needs to know: "Can I trust this person in front of a customer, and can I trust them to build out the solution necessary?" Build each behavioral story so it answers both.

Interviewers look for:

  • AI perspective: A considered view on where the technology is going and where it could go wrong
  • Customer empathy: Whether you show customer awareness even without direct customer-facing experience
  • Communication style: How you explain technical limits to non-technical audiences with clarity and patience
  • Conflict navigation: How you work through disagreement with peers or customers
  • Ownership: Whether you've owned meaningful outcomes end to end
  • Mission alignment: How your motivation and judgment connect to OpenAI’s mission and values

Sample questions

Here are some example questions to prepare for:

  • Tell me about a time you explained a complex technical concept to a non-technical customer or stakeholder.
  • Describe a disagreement with a customer or teammate and how you worked through it.
  • Walk me through a project you owned end to end.

How to prepare for the OpenAI forward deployed engineer interview

  • Practice production-style coding: Work through multi-part challenges where you build a system and extend it. Know your primary language's internals, including iterators, generators, async, and concurrency.
  • Explain technical choices clearly: Practice walking a non-technical listener through a design decision, its tradeoffs, and how it meets their needs. The take-home walkthrough, project deep dive, and behavioral interview all test this skill.
  • Prepare for LLM systems questions: Be ready to reason about retrieval, fine-tuning vs. prompt design, guardrails, and evaluation for production model deployments.
  • Build with the OpenAI API: Ship a small agentic project on OpenAI's API before your loop. It gives you a concrete answer for the recruiter screen and a current project to present in a technical phone screen.
  • Build an evaluation point of view: Prepare a clear answer for how you'd measure whether a deployed model-backed system is working. Interviewers use evaluation thinking to separate strong candidates.
  • Learn how the business and customer work: Study how OpenAI's products are sold and how customers use them. Interviewers look for whether you can turn a customer need into a shipped system.
  • Research how FDE roles differ by company: FDE work at OpenAI differs from FDE work at companies like Scale AI in work style and stakeholder expectations. With a month or more before your loop, talk to working FDEs, attend industry conferences if you can, and tailor your stories to OpenAI’s version of the role.
  • Develop a point of view on AI: Decide where you think the technology is heading and where it carries risk, and be ready to defend that view.
  • Practice with mock interviews: Run full coding, system design, and project rounds with mock interviews so rapid follow-up feels familiar. For targeted feedback, work with an expert coach.

About the OpenAI forward deployed engineer role

OpenAI forward deployed engineers (FDEs) write production code while embedded with OpenAI’s most strategic customers, combining frontier-model engineering with customer delivery. The role spans complex, end-to-end deployments from early scoping through production rollout.

OpenAI also hires AI deployment engineers under a separate title, and those roles generally lean more advisory, though some are hands-on coding roles. Confirm which title your loop is for before you prepare.

OpenAI FDEs typically work on:

  • Embedding with customers to turn ambiguous business goals into concrete technical plans
  • Designing, building, and deploying full-stack systems and custom data pipelines in production
  • Taking on the most complex custom requests customers bring and turning repeat patterns into automated products
  • Building evaluation systems that measure accuracy and catch failures before launch
  • Bringing field insights back to product and research teams to shape the roadmap

OpenAI forward deployed engineer experience requirements

Most OpenAI FDE postings ask for 5+ years of engineering or technical deployment experience that includes customer-facing work, along with hands-on experience building or deploying LLM-powered systems. Industry-specific FDE roles in healthcare, financial services, and government also ask for domain experience.

The role is hybrid, typically 3 days a week in office, with up to 50% travel for customer engagements.

Additional resources

FAQs about the OpenAI forward deployed engineer interview

How many rounds are in the OpenAI forward deployed engineer interview?

The OpenAI FDE interview typically includes a recruiter screen, 1 to 2 early technical assessments, and a final round with 4 to 6 interviewers across 1 to 2 days. The early stage can be a take-home case study with a separate coding screen, back-to-back coding and system design screens, or a single technical phone screen. The exact count varies by team, location, and hiring entity.

Are AI tools allowed in the OpenAI forward deployed engineer interview?

AI tools are allowed in some OpenAI FDE coding interviews and prohibited in others, and OpenAI states the rules for each round in your prep materials. Some FDE coding screens use a Codex-enabled environment, where you share your screen, narrate your reasoning, and explain the time and memory cost of anything the tool writes. Practice with and without AI assistance, and ask your recruiter if the rules aren’t clear.

Does the OpenAI Deployment Company hire forward deployed engineers?

The OpenAI Deployment Company, known as DeployCo, hires forward deployed engineers through its own careers site, with roles in the US, UK, Europe, Australia, Singapore, and the UAE. OpenAI majority-owns and controls the Deployment Company, which embeds FDEs inside enterprise customers to deploy AI systems on OpenAI models. DeployCo’s US FDE roles expect you to own business outcomes as well as the technical build, with a path toward management. Ask your recruiter which entity the role belongs to and whether its interview loop differs from the core OpenAI FDE loop.

Can a software engineer become an OpenAI forward deployed engineer?

Software engineers can move into OpenAI FDE roles, with most of the prep going into learning how the business works: how products are sold, how customers use them, and how deployments create and expand revenue. Study how products develop over time, including the basics of market research, customer research, and sales, and be ready to show customer empathy even if you haven’t held a customer-facing role. OpenAI expects FDEs to learn each customer’s industry on the job; business understanding matters more as coding assistants speed up software development.

How is the OpenAI FDE interview different from the OpenAI software engineer interview?

The OpenAI FDE interview shares the core engineering loop with the SWE interview but adds heavy emphasis on customer-facing judgment and production model deployment. Expect deeper questions on retrieval, evaluation, and scoping ambiguous customer needs, plus a customer-focused take-home.

How long does the OpenAI forward deployed engineer interview process take?

The OpenAI FDE interview process typically takes 3-6 weeks from recruiter screen to decision, depending on the team and whether your loop includes a take-home. OpenAI aims to share a decision within a week of your final interviews.

How much does an OpenAI forward deployed engineer make?

Here are the reported compensation ranges by level for OpenAI software engineers, the closest available benchmark, according to Levels.fyi:

  • L2 (entry): ~$249K
  • L5 (senior): ~$911K

OpenAI also posts base salary bands for forward deployed roles: roughly $145K-$325K across individual contributor FDE and Forward Deployed Software Engineer roles, plus equity. DeployCo posts $170K-$400K for US FDE roles and £90K-£150K in the UK, plus equity.

OpenAI moved new equity grants from Profit Participation Units to RSUs in early 2026, with quarterly vesting and no cliff. Equity value tracks OpenAI’s private valuation, and totals can shift between offer cycles.

Learn everything you need to ace your Forward Deployed Engineer (FDE) interviews.

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