Forward Deployed Engineer
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Diagnose the real customer problem, define success and build the production software foundation
- Ship grounded AI workflows quickly and evaluate whether they actually work
- Productionize securely, drive adoption and turn field learning into reusable platform leverage
Benchmark: OpenAI Forward Deployed Engineer + Palantir Forward Deployed Software Engineer (current Sep 2026) Market refresh: embedded customer delivery; discovery + technical scoping; hands-on full-stack build; production LLM/RAG/agent systems; eval-driven iteration; cloud/security/reliability; adoption; scope-speed-quality tradeoffs; reusable patterns; and field signal back to Product/Research.
Who’s Hiring Right Now
US openings posted in the last 7 days at OpenAI, Palantir — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Enrichment
Diagnose the real customer problem, define success and build the production software foundation
Customer discovery, scoping + measurable outcomes
Coverage: Product Discovery / Problem Solving / Product Metrics
Production software, APIs + event/data integration
Coverage: Python / Full-Stack Development / APIs / Data Engineering
Ship grounded AI workflows quickly and evaluate whether they actually work
RAG, grounding + agent/tool workflows
Coverage: RAG / AI Agents / Prompt & Context Engineering / MCP
Evals, failure analysis + quality regression loops
Coverage: AI Evals & Monitoring / LLMOps & Model Serving
Productionize securely, drive adoption and turn field learning into reusable platform leverage
Reliability, cloud integration + AI security
Coverage: Observability & SRE / Cloud Security / AI & Agent Governance
Client leadership, adoption + reusable delivery playbooks
Coverage: Executive Communication / Change Adoption / Program Risk & Dependency
Role-Readiness Proof / Capstone
Embed with a real or simulated enterprise customer and own one frontier-AI deployment from ambiguity to stable production. Run discovery and write a scoped outcome brief with acceptance criteria; map architecture, data and security boundaries; build a full-stack API/data integration plus grounded RAG or agent/tool workflow; create a representative eval set and regression gates; instrument quality, latency, cost and adoption; deploy with identity/permissions, privacy, observability, SLOs and rollback; manage scope-speed-quality tradeoffs and blockers; deliver an executive demo plus enablement/handoff; then codify reusable components/playbooks and write field feedback that would change a product or model roadmap.
Proof Rubric
- Prove end-to-end customer impact: sharp problem framing and success criteria
- hands-on code and systems integration
- grounded AI behavior
- representative evals and failure analysis
- safe permissions/data boundaries
- reliability, latency and cost evidence
- disciplined scope-speed-quality tradeoffs
- customer adoption and workflow impact
- clear executive/client communication
- maintainable handoff
- reusable abstractions
- and specific field learning for Product/Research rather than a one-off demo