Forward Deployed AI Engineer

Role overview

Join Normal Computing as a Forward Deployed AI Engineer embedded with enterprise semiconductor engineering teams. You will adapt Normal’s platform to customers’ proprietary data and workflows, connect their engineering artifacts to ML systems, and feed insights back to Normal’s core ML and product teams.

Responsibilities

• Adapt Normal’s EDA/platform to each customer’s proprietary data, design flows, and tooling
• Validate generated artifacts against each customer’s specifications
• Bridge customer engineering artifacts and ML systems by diagnosing whether issues stem from:
  • the model
  • the data
  • the workflow
• Codify effective engagement learnings into reusable patterns to improve future deployments
• Bring field signal back to Normal’s core ML and product teams to guide investment priorities

Requirements

• Comfortable in ambiguity; able to decide with incomplete information and know when to ask vs. act
• Hands-on experience with modern ML stack, including:
  • prompt engineering
  • fine-tuning
  • evals
  • RAG
  • agentic patterns
  • model deployment
• Ability to go deep into semiconductor verification workflows, including spending substantial time in UVM testbenches, SystemVerilog, and design specifications
• Strong software engineering fundamentals:
  • Python
  • comfort writing production-quality code
  • distributed-systems literacy
• Evidence of shipping ML systems in customer/production environments where real-world data impacted model behavior

Nice to have

• Direct experience with EDA, semiconductor design flows, or verification workflows (e.g., UVM, SystemVerilog, coverage-driven verification)
• Experience building/leading an FDE/customer-deployment function
• Open-source contributions or publications in AI/ML
• Background in hardware/electrical engineering or semiconductor verification