Forward Deployed Engineer, Research

About the role

Location: San Francisco (In-Person) OR New York (In-Person, with an initial 3–6 month rotation in SF)

About the Role:

We are looking for a Forward Deployed Engineer (FDE) who builds like a core systems engineer and communicates like a peer to elite researchers. You will embed directly with technical teams at customer organizations—working shoulder-to-shoulder with their machine learning engineers, researchers, and infrastructure leads—to architect, code, and deploy complex AI agent systems into production.

This is not a traditional solutions engineering or technical account management role. You will write high-volume, production-grade code, solve non-trivial architecture challenges in real time, and push the limits of what automated agent workflows can accomplish in enterprise environments.

What You'll Do

Code & Ship Systems: Design, build, and deploy production-ready AI agent architectures tailored to high-complexity customer environments (think Decagon, Sierra, Harvey, or Glean-style workflows).

Engage Technical Peers: Lead deep technical discussions, code reviews, and architecture reviews directly with client engineers and ML researchers, establishing immediate credibility.

Bridge Product & Customer: Extract core engineering patterns from client deployments to inform and directly contribute to our core platform and product roadmap.

Unblock Deployment Pipelines: Diagnose performance bottlenecks, hallucination vectors, state-management failures, and latency issues across high-throughput agent pipelines.

What We're Looking For

Agent Architecture Experience: Proven track record building complex agentic systems, tool-use frameworks, autonomous workflows, or high-density retrieval/reasoning engines.

Hands-on Technical FDE Track Record: Prior experience in a forward-deployed engineering role where you explicitly wrote production code, or an engineering role with a heavy customer-facing edge.

Stack-Agnostic Software Fundamentals: Strong foundation in computer science, system design, data structures, and production backend code—regardless of specific language or framework.

Engineering Credibility: Ability to go white-board-to-white-board on LLM internals, evaluation frameworks, memory management, and edge cases with top-tier AI researchers and engineers.