The Forward Deployed Engineer (FDE) is the fastest-growing engineering role in enterprise tech. FDE job postings grew 800%+ in 2025. Companies including Palantir, OpenAI, Anthropic, Salesforce, Databricks, and Adobe are all hiring for it. Most hiring managers and engineering leads have never recruited for it before.
This guide gives you a copy-paste job description template, a breakdown of what the role actually involves, how to screen candidates correctly, and the interview questions that separate strong FDEs from the wrong hires.
Forward deployed engineer job description template
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Job title: Forward Deployed Engineer
Location: [Location / Remote / Hybrid]
About the role
We’re looking for a Forward Deployed Engineer to embed directly with our enterprise customers and lead complex, high-stakes deployments of [product/platform]. This isn’t a solutions or pre-sales role. You’ll write production code inside customer environments, own the technical outcome of each engagement, and bring what you learn back to our product team.
You’ll work on a rotation model: a defined period embedded with one customer, then time back with our core engineering team. Both phases matter. The best FDEs improve the product as much as they improve the deployment.
What you’ll do
- Embed with enterprise customers to understand their technical environment, constraints, and operational workflows
- Scope, architect, and build production-grade solutions using [product/platform] inside customer systems
- Own the full deployment lifecycle from discovery through to production rollout and handover
- Communicate technical decisions and tradeoffs clearly to both engineering teams and executive stakeholders
- Identify product gaps and surface structured feedback to our internal product and research teams
- Maintain current knowledge of LLM capabilities, agentic patterns, and relevant developments in AI tooling
What we’re looking for
- 5+ years of production engineering experience, with at least some of it in customer-facing or deployment-heavy roles
- Demonstrated experience building or deploying systems powered by LLMs or generative AI models
- Strong systems design capability — you can scope a complex integration before you start building it
- A track record of owning relationships with technical and non-technical stakeholders at senior levels
- Comfort operating in ambiguous environments where the requirements change mid-engagement
- Clear, direct communication — you can explain a technical decision to a CTO and a CFO in the same conversation
Nice to have
- Experience working inside enterprise environments with legacy systems, complex data pipelines, or regulated infrastructure
- Prior experience in a forward deployed, solutions engineering, or technical delivery role
- Familiarity with [specific stack, tools, or frameworks relevant to your product]
What success looks like
In your first 90 days, you’ll complete at least one full customer engagement: discovery, build, production rollout. You’ll have filed structured product feedback from that engagement. You’ll have contributed to at least one internal engineering workstream between deployments.
What an FDE actually does
The role was created by Palantir in the early 2010s. Palantir embedded engineers directly inside government agencies and large enterprises not just to consult, but to build. “Forward deployed” means sent into the field, ahead of the main force, to solve problems that can’t be solved remotely.
Four functions define the role across every company that runs this model:
Discovery and scoping
FDEs map what the customer actually needs, not what they said in the sales call. Enterprise environments are messier than any pre-sales discovery reveals. FDEs find the constraints that matter before they start building.
Production engineering
This is the hard line between an FDE and a solutions engineer. FDEs write and ship production code inside the customer’s environment. Not demos, not prototypes handed to someone else.
Stakeholder management
FDEs work across two distinct audiences simultaneously: the customer’s engineering team (who need technical depth) and the customer’s executive stakeholders (who need clear outcomes). Strong FDEs are credible with both.
Internal feedback loop
FDEs bring structured customer intelligence back to the product team. At OpenAI, this feedback directly shapes model and product roadmaps. The rotation model exists because information exchange is part of the job.
How it differs from the roles it resembles
Most CVs you receive will come from solutions engineers, solutions architects, and implementation consultants. Here’s how to tell them apart:
| Role | Typical primary value |
| Sales engineer | Helps close deals |
| Solutions architect | Designs technical solutions |
| Implementation consultant | Gets customers operational |
| Forward deployed engineer | Builds customer-specific production systems while influencing product direction |
What strong FDE candidates look like
The hardest part of hiring for this role is that strong FDEs are rare because the combination is rare. Most engineers with deep technical ability haven’t owned senior customer relationships. Most people with strong client management backgrounds aren’t production engineers.
What to look for:
A non-negotiable technical floor
Five or more years of production engineering experience, with clear examples of LLM or generative AI deployment. Candidates should be able to discuss systems design decisions at a level that your best engineers respect.
Owned customer relationships, not participation
“I was part of the team that worked with the customer” is not the same as “I owned the technical outcome for that customer.” Look for candidates who can describe specific decisions they made, specific pushback they navigated, and specific stakeholders they managed.
Speed under incomplete information
FDEs scope and build in compressed timelines with requirements that change. Look for evidence of this in their work history, not just their answers.
Low ego with high standards
The best FDEs don’t need to be the smartest person in the room. They focus on the customer’s problem, not on demonstrating their own capability. This is difficult to screen for but shows up clearly in how candidates talk about their previous work.
Interview questions that test for FDE ability
Standard engineering interviews don’t always surface the skills that make an FDE effective. Add these to your process:
“Walk me through a time you had to build something for a customer whose requirements changed significantly after you’d already started.”
You’re looking for candidates who adapted without losing the customer relationship.
“How do you decide whether a customer’s problem needs an engineering fix or a product conversation?”
Strong FDEs distinguish between deployment problems and product gaps. Weak candidates treat every customer complaint as a technical problem to solve.
“Describe a time you delivered bad news to a senior stakeholder. What did you say, and what happened?”
FDEs routinely tell customers that what they want isn’t buildable, or isn’t advisable. Candidates who’ve never done this are a significant risk.
“How many production systems have you worked inside that you didn’t build yourself?”
The answer tells you a lot about how comfortable a candidate is in unfamiliar codebases under customer pressure.
For the technical screen, give candidates a realistic deployment scenario and ask them to scope it before they write a line of code. The scoping conversation reveals more than any coding exercise.
Compensation and career trajectory
FDE salaries are high because the combination of skills is scarce, at least right now. Total compensation at US-based AI companies ranges from $200,000 to $350,000+ at the senior end, though UK and European benchmarks are meaningfully lower. Verify current ranges on Levels.fyi or with a specialist tech recruiter before you set your band.
Career progression can go many different want, but a sample path runs: FDE → Senior FDE → Principal FDE → Technical Solutions Architect → VP of Solutions Engineering.
Some FDEs move into core product engineering or product management after their first few deployment cycles, particularly at companies where the feedback loop to the product team is strong.