The short answer
If the code you ship lives in the customer's repository and the win is the customer's outcome, that is forward deployed work. If the code you ship is a demo or integration that helps close and onboard a customer onto your employer's platform, that is solutions engineering.
The two roles look similar from the outside because both put a technical person in front of a customer. The forward deployed engineer vs solutions engineer question comes down to whose product gets built and who you answer to.
Side by side
| Dimension | Forward Deployed AI Engineer | Solutions engineer | Applied AI engineer |
|---|---|---|---|
| Works for | The customer's outcome, via a deployment company or a direct engagement | The software vendor selling the product | Their own company |
| Primary deliverable | A production AI workflow in the customer's codebase | Demos, proofs of concept, and integrations | AI features inside their own company's product |
| Where the code lives | The customer's product and repository | The vendor's platform and demo environments | Their employer's codebase |
| Measured on | A shipped workflow the customer's team can run and maintain | Pipeline, deals closed, and successful onboarding | Shipping AI capabilities in-house |
| AI focus | Prompt and context design, data integration, evaluation rubrics | Showing how the vendor's AI features fit the customer's stack | Building and evaluating AI features for their own product's users |
| Compensation | Base typically $160,000 to $220,000, median near $190,000; median US total compensation about $206,000; well above that at frontier labs, mostly in equity (sources below the table) | Varies by vendor; typically base plus commission (OTE), tied to sales and onboarding outcomes. | Varies by company and level |
Job postings in 2026 put a forward deployed engineer's base salary typically between $160,000 and $220,000, with a median near $190,000 (Recruiting from Scratch). Median US total compensation is about $206,000 (Levels.fyi). Pay at frontier labs such as Anthropic and OpenAI runs well above that, mostly in equity. Hiring for a small team? See what a forward deployed engineer costs a startup.
Where they overlap
Both roles need to read a customer's real environment quickly, translate a business problem into a technical plan, and stay credible in a room with engineers and executives at the same time. Both live with ambiguity and incomplete data. The instincts transfer cleanly, which is why solutions engineers are one of the most common backgrounds for people moving into forward deployed AI work.
How to choose between them
Choose by what you want to own. If you want to close deals and help many customers adopt one product, solutions engineering fits. If you want to build the actual feature and own whether it works in production, the Forward Deployed AI Engineer role fits.
One more practical signal: seen as solutions engineer vs forward deployed engineer, the trade is breadth against depth. Solutions engineering rewards breadth across a pipeline; forward deployed work rewards depth on one customer at a time. Neither is more senior than the other. They optimize for different outcomes.
Applied AI engineer vs solutions engineer vs forward deployed engineer
An applied AI engineer usually builds AI features into their own company's product, a solutions engineer sells and integrates a vendor's product, and a forward deployed engineer ships AI into a customer's product. The difference is whose codebase the work lives in: the employer's, the vendor's demo environment, or the customer's. One exception: at Anthropic, Applied AI is the customer-facing team, so an applied AI engineer there does forward deployed work. When a posting says applied AI, check whose product you would be building.
Related
Frequently asked questions
Can a solutions engineer become a Forward Deployed AI Engineer?
Often, yes. Solutions engineers already have the customer-facing instinct and the integration skills. The shift is from selling and configuring a vendor's product to building and owning a production AI workflow inside the customer's codebase, plus the evaluation discipline to prove it works.
Who does each role work for?
A solutions engineer almost always works for the software vendor and is measured on sales and onboarding. A Forward Deployed AI Engineer works on the customer's outcome, whether employed by a deployment company or engaged directly, and is measured on shipping a workflow the customer's team can run.
Does a solutions engineer write production code?
Sometimes, but usually demos, proofs of concept, and integrations rather than features that live permanently in the customer's product. A Forward Deployed AI Engineer's main deliverable is production code in the customer's repository, with edge cases, logging, and a handoff.
What is the difference between an applied AI engineer and a solutions engineer?
An applied AI engineer usually builds AI features inside their own company's product and is measured on shipping them; a solutions engineer sells, demos, and integrates a vendor's product for customers and is measured on the deal. At Anthropic the Applied AI title names the customer-facing team, which is closer to forward deployed work.