Forward Deployed Engineer, Normal Computing

• 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
• You thrive as a problem-solver and take pride in winning over our customers along with the rest of your team

Desirable

• The cost of taping out silicon is expensive and it is harder for companies to avoid multiple tape-outs due to the complexity of verifying silicon
• 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 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 will be debugging distributed systems, post-training models and have some understanding of System Verilog since that is the language of our customers
• Own the problem-solving in production. 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
• Design and run evals against real customer workflows, validating generated artifacts against their specifications so model behavior holds up in production
• Integrate the platform with each customer's data, design flows, and tooling, working with their production codebases and against their existing infrastructure
• Embed with silicon design teams, translate their constraints into model and platform requirements, and carry that signal back to Normal's research, product, and platform teams to shape what gets built next
• Post-train Normal's models on each customer's 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
• Make the calls on what to build, what to skip, and when to push back on a request that would compromise what ships, often ahead of any playbook. Codify what works into patterns that raise the floor for every engagement after yours
• Work with the greater engineering team to make our tools better utilizing the valuable knowledge you have acquired in your interactions with customers