5-7 years of experience shipping ML systems in production environments.
Background in hardware or electrical engineering, particularly in semiconductor design.
Proficient in semiconductor verification workflows, with an emphasis on UVM and SystemVerilog.
Strong software engineering fundamentals, especially in Python.
Hands-on experience with modern ML techniques and deployment practices.
Ability to navigate ambiguity and make informed decisions with limited information.
Responsibilities
Adapt Normal EDA to fit customer-specific data and workflows.
Diagnose issues between customer artifacts, data, and models.
Embed with customer engineering teams to gather insights and feedback.
Make strategic decisions about project priorities and customer requests.
Document successful patterns for enhancing future customer engagements.
Relay customer needs back to core ML and product teams to influence development.
Benefits
Collaborative work environment with cross-functional teams.
Opportunity to work with cutting-edge ML technology in the semiconductor space.
Involvement in shaping the future of product development based on customer feedback.
Ability to impact silicon engineering practices directly at client sites.
Full Job Description
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 calls on what to build, what to skip, and when to push back on a customer request that would compromise the quality of what ships. You will regularly operate ahead of a playbook.
Codify what works on each engagement into reusable patterns that raise the floor for every engagement after yours.
Bring field signal back to Normal's core ML and product teams. Your read on what customers actually need will directly shape model and platform investment priorities.
What Makes You A Great Fit
Track record of shipping ML systems inside customer or production environments where model behavior had to hold up against real-world data. You may have been called an AI FDE, an Enterprise Tech Lead, or something else entirely.
An interest in hardware, electrical engineering, and how chips get designed and built. If your background is in semiconductor verification, hardware engineering, or a related domain and you are building ML depth, that combination is a strong fit for this role.
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.
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.
Calm in ambiguity. You make good decisions with incomplete information, and you know when to act and when to ask.
Bonus Points For
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.
