A forward deployed engineer (FDE) is a software engineer who works embedded with one customer, on site or inside the customer's own tools, and builds and ships software against that customer's real problem until it works in production. A product engineer builds one product for many customers. An FDE does the reverse: many capabilities for one customer, with the outcome as the job. Palantir, which made the title famous, describes the split the same way: its product engineers build one capability for many customers, and its forward deployed engineers enable many capabilities for one customer (Palantir, A Day in the Life of a Forward Deployed Software Engineer). If you are hiring for the role, read on. If you are buying the work rather than the headcount, the last section covers hiring a forward-deployed team instead of an FDE.
Where the role came from
The title comes from Palantir. Its two largest engineering roles are Software Engineers and Forward Deployed Software Engineers, known internally as "Devs" and "Deltas". Palantir's own engineering blog says the name Delta is a leftover from its early days, when each business development team was named after a letter of the NATO alphabet (Palantir, Dev versus Delta). Devs build the core platforms. Deltas sit with customers in defense, government, healthcare and industry, and turn those platforms into working systems for one organisation's problem.
The idea is older than the title. Marty Cagan of SVPG argues that sending a technical person to embed with the target customer, to understand the environment well enough to find a solution that delivers the outcome, is good product practice in general, and credits Steve Blank and Bill Campbell with similar arguments (SVPG, Forward Deployed Engineers). His preferred form sends one FDE to several customers so the patterns they find can become a single product.
What a forward deployed engineer does
The work is software engineering, done in someone else's building. A typical week covers five kinds of task:
- Discovery. Sitting with the people who do the work today, reading the data, and finding the one workflow where software will change a number the customer cares about.
- Scoping. Turning that into a build the customer can approve: what ships, what it connects to, and how success is measured.
- Building. Writing production code against the customer's systems, data and security rules. Integrations take more of the time than the model or the interface.
- Shipping. Getting the system into production and into daily use, which includes access reviews, change control and training.
- Feeding back. Telling the product team what customers keep asking for, so the next customer gets it as a feature.
Job postings show the same shape. OpenAI's FDE listings ask the engineer to own discovery, technical scoping, system design, build and production rollout, with success judged by production adoption and measurable impact on the customer's workflow (OpenAI careers, Forward Deployed Engineer). Anthropic's Applied AI team says its FDEs embed directly with its most strategic customers to drive AI adoption, building production applications with Claude inside customer systems and travelling to customer sites 25–50% of the time (Anthropic, Forward Deployed Engineer, Applied AI).
Forward deployed engineering in AI
An AI forward deployed engineer does the same job with large language models in the stack. Bloomberry's analysis of about 1,000 FDE job postings found that the most requested skills were Python (66% of postings), TypeScript (35%) and AWS (32%), with AI agents (35%) and LLM experience (31%) leading the AI skills. Working directly with customers appeared in 55% of postings (Bloomberry, I analyzed 1,000 forward deployed engineer jobs). The model itself is rarely the hard part. Connecting it to the customer's data, adding guardrails, writing evaluations and getting a nervous compliance team to sign off is the job.
FDE vs solutions engineer vs consultant vs staff augmentation
The four roles get confused because all of them put a technical person in front of a customer. The difference is what they are paid to deliver.
| Role | Main job | Writes production code? | Measured on | Usually paid by |
|---|---|---|---|---|
| Forward deployed engineer | Build and ship a working system for one customer's problem | Yes, most of the week | The system in production and the customer's number moving | The vendor, as part of the deal |
| Solutions or sales engineer | Show that the product fits, before the sale | Demos and proofs of concept | Deals won | The vendor's sales budget |
| Management or IT consultant | Diagnose, recommend, sometimes implement | Sometimes | Deliverables in the statement of work | The customer, by the day or project |
| Staff augmentation contractor | Add capacity to the customer's own team | Yes, on tickets someone else wrote | Hours billed | The customer, by the hour |
The useful test is ownership. A solutions engineer is finished when the contract is signed. A consultant is finished when the deliverable is accepted. A staff augmentation contractor is finished when the hours run out. A forward deployed engineer is finished when the system works in production and people use it. How the forward-deployed model differs from staff augmentation and a fixed statement of work, with prices, is on our comparison page.
Why AI companies are hiring FDEs now
Demand for the title has grown fast. Bloomberry counted postings titled "forward deployed engineer" from January to October 2025 and found 1,165% more than in the same months of 2024, with October 2025 the highest month on record at the time (Bloomberry). Different counts give different multiples, so treat the exact figure as one data point. The direction is not in doubt.
Three things explain it:
- AI products do not install themselves. A model with an API is a capability. A support agent that resolves tickets inside one company's help desk, with its policies and its edge cases, is a project. Somebody has to do that project, and AI vendors have decided it should be their own engineers.
- The value sits in the customer's data and workflow. Generic demos are easy to copy. The deployment that works on a customer's real records is hard to copy, and it is where renewals come from.
- Discovery has to happen in the field. Cagan's point applies directly: startups building AI agents adopt the model because they need deep customer context to find what is worth building (SVPG).
Skills and pay
The role asks for a full-stack engineer who can also run a meeting. Postings ask for production experience with Python or TypeScript and a major cloud, recent LLM work, and comfort working directly with customer executives. OpenAI's London FDE listing asks for 5+ years of engineering or technical deployment experience (OpenAI careers). On pay, Bloomberry puts the median at $173,816 across postings that disclosed a salary range (Bloomberry), and one Anthropic Applied AI posting listed a base salary of $200,000–$300,000 (Anthropic posting). The frontier-lab posting sits well above that median.
Looking for an FDE job rather than an FDE? Gigabit hires engineers who want to work this way. See open roles.
Hiring a forward-deployed team instead of an FDE
Most mid-market companies do not need to employ an FDE. They need the result an FDE produces: a system built inside their business, against their real problem, shipped to production and owned until it works. Hiring one senior engineer with that profile is slow and expensive, and one person cannot cover design, back end, integrations, evaluations and operations alone.
That is the gap Gigabit fills. Gigabit is an AI-native software factory, and forward deployment is how it works: senior engineers embed in your team, your repos, your Slack and your board, and build against your problem with AI agents doing the repetitive work. You get the forward-deployed model as a team, priced by the month, instead of a single hire you have to find, pay and keep. How the model runs is written up step by step.
| Hire one FDE | Embed a forward-deployed team | |
|---|---|---|
| Time to start | A senior search, often months | Assembled in about 2 weeks, first pull requests by Day 5 |
| Coverage | One person's skills | A pod matched to the work: full-stack, AI, QA, data |
| Cost | A US senior salary plus equity and benefits | $3,000–$7,500 per engineer per month |
| Commitment | Permanent headcount | 2–4 week trial, no lock-in |
| Who owns the outcome | You, through the hire | Shared: your owner sets the bar, the pod ships to it |
What it looked like for one client
A Series B HR tech company had two senior engineering seats open for four months and had lost three offers. Three embedded Gigabit engineers cost $14,400 a month. Their first pull requests landed in 5 days, sprint velocity rose 2.3× (34 to 78 points) within the quarter, and the company saved $392,400 (65%) a year against the US hires it had priced (full case study). That is one engagement, not a promise. It shows what to demand from any forward-deployed team: named engineers against a written spec, a pull request in your repo in the first week, and a trial before you scale.
When to hire your own FDE instead
Hire the FDE yourself when you are a software vendor and forward deployment is part of your product: your engineers need to sit with your customers, learn what they need, and carry it back to your roadmap. That knowledge should stay in your company. Embed a team when you are the customer: you have a problem to solve, a backlog longer than your team can finish, and no time to spend a quarter recruiting. If you are weighing the two, when to hire vs embed AI engineers walks through the numbers.
The short version
A forward deployed engineer is an engineer who works inside a customer's business and owns the result until the software runs in production. Palantir made the title. AI companies grew demand for it more than tenfold in 2025. If you are buying the outcome rather than the headcount, Embedded AI Teams give you the forward-deployed model at $3,000–$7,500 per engineer per month, with first pull requests by Day 5 and no lock-in.
