AI forward deployed engineer
Same FDE craft — embed with the customer and ship — aimed at AI products: model platforms, coding agents, data/AI stacks. We currently see 1,502 listings on this board that mention AI/ML in the title or company context.
What changes in an AI FDE role
You still integrate, debug, and earn trust on-site. The difference is the product surface: evals, retrieval, agent workflows, GPU/infra constraints, data governance, and buyers who are still inventing the use case. You are teaching the customer how to work with the product as much as you are wiring it up.
Skills that actually get screened
- Product + model literacy — enough to scope a use case, spot hallucination/risk issues, and say no to bad fits
- Integration engineering — APIs, auth, data pipelines, VPC/private networking, identity
- Eval thinking — offline metrics are not enough; you need task-level success criteria with the customer
- Change management — rolling AI into an engineering org without breaking trust
- Writing — crisp updates for technical and executive audiences
Companies hiring this flavor
From the current feed (AI/ML keyword match): Databricks, OpenAI, Zscaler, Cloudflare, Datadog, ElevenLabs, MongoDB, Snowflake. Also browse hubs for OpenAI, Databricks, and Palantir.
How interviews usually feel
Expect a mix of coding, system design for a customer scenario, and “tell me about a time you unblocked a skeptical stakeholder.” AI labs often care about taste and written communication as much as leetcode. Bring one concrete story where you took ambiguous customer pain to a shipped integration.
Related reading
What is an FDE? · FDE vs solutions architect · State of FDE Hiring