Forward Deployed Engineer

Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic in-memory asynchronous: the result is 10-100 more AI inference per dollar per watt.

We co-design the full stack: AI-native EDA systems in production with the worlds largest semiconductor companies and the advanced ASICs they make possible. Backed by $85M from the worlds leading deep-tech investors and built by scientists engineers and operators from the labs that built modern computing.

Normal works as one team across New York Silicon Valley London Copenhagen and Seoul. We hire people who want the hardest version of their craft across every discipline at every seniority.

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 silicon engineering lifecycle learning continuously from the teams that use it. As a Forward Deployed Engineer you own our EDA system inside a customers 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 post-training models and working in SystemVerilog because that is the language of our customers.

• Production Problem-Solving: Diagnose issues in our system the model the data or the workflow. Work deep in both Normals systems and the customers 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 customers data design flows and tooling working with their production codebases and against their existing infrastructure.

• Customer Signal: Embed with silicon design teams translate their constraints into model and platform requirements and carry that signal back to Normals research product and platform teams to shape what gets built next.

• Continual Learning: Post-train Normals models on each customers proprietary data and trajectories and 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.

• Great at problem-solving and tracking down issues wherever they are in the stack

• Strong software engineering fundamentals: proficient in Python comfortable in production codebases distributed-systems literate

• Hands-on experience with the modern ML stack: prompt engineering fine-tuning evals RAG agentic patterns model deployment

• Willingness and ability to go deep on semiconductor verification workflows. You will spend significant time inside UVM testbenches SystemVerilog codebases and design specifications. Prior experience is a strong advantage but what matters is whether you can build fluency fast and earn credibility with verification engineers

• An ability to ship ML systems inside customer or production environments where model behavior had to hold up against real-world data

• Calm in ambiguity: you make good decisions with incomplete information and you know when to act and when to ask

• Comfortable with travel when needed

• Direct experience with EDA semiconductor design flows verification workflows (UVM SystemVerilog coverage-driven verification) or other formal/structured engineering domains

• Built or led an FDE or customer-deployment function from the ground up at an earlier-stage company

• Open-source contributions or publications in AI or ML venues

Equal Employment Opportunity Statement

Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race color religion sex sexual orientation gender identity national origin disability veteran status or any other legally protected status.

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Required Experience:

IC