Normal Computing
Semiconductor
ForwardDeployedAIEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Forward Deployed AI Engineer at Normal Computing. Skills: ML systems, Python, Semiconductor verification, Customer environments. Adapt Normal EDA to customer data. Validate generated artifacts”
Industry & Context.
Diagnose issues; Make good decisions with incomplete information
What They're Looking For.
Must Have
Track record of shipping ML systems inside customer or production environments, software engineering fundamentals, Hands-on experience with the modern ML stack
Nice to Have
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
What You'll Do.
Adapt Normal EDA to customer data
Validate generated artifacts
Bridge customer artifacts and ML systems
Embed with silicon engineers
Make judgment calls on builds
Codify reusable patterns
Bring field signal to ML teams
How You'll Work.
Team & Collaboration
Work as one team across locations; Work inside a pod with Deployment Strategy, Platform, other FDE, and GTM team members; Translate customer constraints back to Normal's teams; Shape platform based on field learning
Full Job Description
NORMAL COMPUTING | INCREDIBLE OPPORTUNITIES The Normal Team builds foundational software and hardware that help move technology forward, supporting the semiconductor industry, critical AI infrastructure, and the broader systems that power our world. We work as one team across New York, San Francisco, Copenhagen, Seoul, and London. YOUR ROLE IN OUR MISSION Silicon engineering is defined by exponential growth in design complexity, shrinking first-silicon success rates, and a global shortage of specialized talent. Normal EDA addresses these constraints with a platform that builds structured, traceable engineering artifacts directly from specifications and learns continuously from the teams that use it. The Forward Deployed Engineer is the person who makes that work inside customer environments, adapting the platform to their data, their workflows, and the engineering judgment their teams carry. We are hiring Forward Deployed AI Engineers to embed inside enterprise customers' silicon design environments and adapt Normal EDA to their data, workflows, and design challenges. You will work inside a pod with Deployment Strategy, Platform, other FDE, and GTM team members on a single enterprise engagement, and you will own the ML systems that make the platform work inside that customer's environment. RESPONSIBILITIES - Adapt Normal EDA to each customer's proprietary data, design flows, and tooling. Validate generated artifacts against their specifications, and design evals against real customer workflows so model behavior holds up in production. - Bridge between customer engineering artifacts and the ML systems that operate on them. You will need to understand both sides well enough to diagnose whether a problem is in the model, the data, or the workflow. - Embed with silicon engineers at the customer. Translate their constraints back to Normal's research, product, and platform teams, and shape what the platform becomes based on what you learn in the field. - Make judgment cal
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