About the job
We are hiring a Forward Deployed Engineer (FDE) to lead end-to-end deployments of OpenAI’s models inside semiconductor and chip design organizations. You will work with customers who are deep experts in hardware architecture, RTL, verification, and performance engineering, translating complex workflows, massive codebases, and long-running toolchains into production AI systems.
Responsibilities
Design and ship production AI systems around models, owning integrations with RTL repositories, verification environments, simulators, and internal tooling.
Lead discovery and scoping from pre-engagement through production rollout, translating ambiguous engineering pain points into hypothesis-driven use cases with measurable outcomes.
Deliver AI-powered verification workflows such as change-aware test selection, directed test generation, and intelligent regression triage, taking them from prototype to daily production use.
Build systems that operate over large, evolving codebases and artifacts (RTL, tests, logs, waveforms, traces), where performance, latency, and failure handling shape architecture.
Define and run evaluation loops that measure model and system quality against workflow-specific benchmarks (e.g., coverage, false positives, debug time, iteration speed).
Own delivery state across multiple workstreams, making trade-offs between scope, speed, and robustness to protect production impact.
Distill deployment learnings into hardened primitives, reference implementations, playbooks, and tooling that can be reused across customers.
Surface field insights that inform model behavior, tooling gaps, and future product direction across the semiconductor stack.
Qualifications
Minimum
Bring 5+ years of engineering experience in chip design, verification, EDA, or FPGA development (including RTL design, timing closure, and hardware/software co-design), or closely adjacent systems domains such as firmware, distributed systems, compilers, or performance-critical infrastructure.
Have worked directly with RTL, verification environments, simulators, or large-scale performance/debug tooling — or have partnered closely with teams who do.
Have delivered complex systems end-to-end in environments where scale, correctness, and long feedback loops shaped how you build and ship.
Write and review production-grade code in Python and/or systems-adjacent languages, and are comfortable integrating across heterogeneous toolchains.
Have experience deploying or experimenting with LLM-powered systems and understand how model behavior, evaluation, and guardrails affect trust and adoption.
Communicate clearly with hardware engineers, software engineers, product teams, and executives, translating technical trade-offs into delivery decisions.
Apply systems thinking with high execution standards, turning failures, regressions, and unexpected model behavior into improved operating patterns.
Stay calm and decisive in technically deep, high-stakes environments where progress depends on credibility and follow-through.
Preferred
No preferred qualifications listed.