Forward Deployed Engineer

Normal Computing | Build with Us Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul. The Role The cost of taping out silicon is enormous, and the complexity of verification makes multiple tapeouts hard to avoid. Normal EDA accelerates this work as an AI platform for collaborative silicon engineering: a single source of truth across the chip lifecycle, learning continuously from the teams that use it. As a Forward Deployed Engineer, you own our EDA system inside a customer's environment. Embedded directly with our partners, you adapt our platform to their data, workflows, and design challenges, working alongside our account executive and a deployment strategist to make the deployment a success. You thrive as a problem-solver and take pride in winning over customers along with the rest of your team. You will be debugging distributed systems, building new product features, post-training models, and working in various silicon-native languages such as SystemVerilog. Note that many different kinds of candidates could be well-qualified for this [... source excerpt omitted ...] ilicon background). What You Will Own Production Problem-Solving: Diagnose issues in our system, the model, the data, or the workflow. Work deep in both Normal's systems and the customer's environment to resolve them, and close the loop with their engineers. Evaluation Against Reality: Design and run evals against real customer workflows, validating generated artifacts against their specifications so model behavior holds up in production. Platform Integration: Integrate the platform with each customer's data, design flows, and tooling, working with their production codebases and against their existing infrastructure. Customer Signal: Embedded with silicon design teams, trans [... source excerpt omitted ...] and carry that signal back to Normal's research, product, and platform teams to shape what gets built next. Continual Learning: Post-train Normal's models on-prem on proprietary customer data and trajectories to customize to their workflow, tooling, and style preferences. Build the continual-learning loops that turn their engineers' feedback into system knowledge, so model quality compounds across the engagement. Judgment Ahead of Playbook: Make the calls on what to build, what to skip, and when to push back on a request that would compromise what ships. Codify what works into patterns that raise the floor for every engagement after yours. What Makes You a Great Fit Great at