Hire a forward deployed engineer when the critical work is getting your product to deliver value inside a customer's environment. Define the balance between coding, customer discovery, deployment, and reusable product work before starting the search.
The title is used differently across companies. This guide offers a role-design and interview framework; it does not claim every employer defines an FDE the same way.
What a useful Refery recommendation included
In one engineering search, a Refery recommendation highlighted hands-on delivery, end-to-end ownership and customer work. It explained why the candidate preferred the role, then identified the depth of work with technical customer counterparts as something to probe in interview.
That is more useful than treating “customer-facing” as a complete assessment. A person may communicate clearly without having owned the implementation. They may have delivered an integration without having worked through the same level of technical customer discussion your role requires.
For each recommendation, ask for the evidence and the unresolved question together. Read the anonymized search account. The described candidate example is not identified as the eventual hire.
Distinguish the job from adjacent roles
| Primary responsibility | Role profile to investigate |
|---|---|
| Build reusable features in the core product | Product or software engineer |
| Develop and evaluate AI capabilities | Applied AI engineer |
| Help prospective customers evaluate technical fit | Solutions or sales engineer |
| Implement, integrate, and improve the product in customer environments | Forward deployed engineer |
One person can span these areas, but the brief should state which responsibility takes priority when they compete. Do not use a broad title to avoid deciding what the job is.
Define the customer environment
Explain the systems the engineer must integrate, the data they can access, the deployment constraints, and who owns security and operational decisions. State travel, on-site work, and customer availability expectations explicitly.
For an AI product, describe how quality will be evaluated, who reviews failure cases, and what happens when the system cannot safely or reliably complete a task. Separate model performance from the surrounding integration and user workflow.
Use an evidence-based scorecard
| Dimension | Interview evidence | Follow-up question |
|---|---|---|
| Discovery | Translates a customer complaint into a testable problem | What information changed your first diagnosis? |
| Engineering | Can explain implementation and integration decisions | Which parts did you personally build? |
| Deployment | Plans access, rollout, monitoring, and recovery | What was your rollback or fallback plan? |
| AI evaluation, if relevant | Defines failure cases and release criteria | How did you compare the system with the prior workflow? |
| Customer communication | Explains constraints and negotiates scope | What did you tell the customer you could not promise? |
| Product judgment | Distinguishes reusable improvements from one-off work | What went back into the core product? |
Record what is confirmed and what remains uncertain. Confident communication is useful, but it is not evidence that the person can implement a production integration.
Run a realistic, bounded exercise
Provide a fictional customer with a data source, an integration constraint, and a desired outcome. Ask the candidate to identify unknowns, sketch an implementation, propose evaluation criteria, and explain a rollout plan.
Use synthetic data. State the time limit and assessment criteria before the exercise. The purpose is to observe judgment, not to obtain unpaid customer delivery.
Examine a difficult deployment
Ask about a project that did not go to plan. Explore the technical failure, the customer communication, what changed, and which decisions the candidate owned. With the candidate's permission, ask a former collaborator about the same work.
Prepare the search brief
Include customer type, product maturity, implementation responsibilities, coding expectations, location and travel, budget, reporting line, and interview stages. Use the startup hiring brief template. If the main need is creating the core product, compare this with the founding engineer guide.
Hiring with Refery
Refery helps early-stage startups hire engineering, AI, and GTM talent through introductions from founders, operators, investors, and specialist recruiters, with screening for the role. The current service and pricing are 15% to 25% of first-year base salary, paid only when you hire; scope and fee are agreed upfront.
Share your role with Refery, or use the hiring brief template to prepare your search.
For the providers who work these searches, see recruiters and platforms that specialize in AI startups.