AI Engineering Lead, Normal Computing

• This is a hands-on leadership role for someone who has grown from strong individual contribution into technical leadership or management
• You should be comfortable moving between architecture, model behavior, evaluation, implementation tradeoffs, hiring, and team development
• The strongest candidates will have built meaningful AI systems in technical domains where models need to operate against real constraints
• Experience with LLMs, RL, agents, ML infrastructure, optimization, model evaluation, or applied AI/ML systems is especially relevant
• Direct experience across AI engineering, ML engineering, applied AI, production ML systems, AI infrastructure, or closely related areas
• Experience as a technical lead, staff-level IC, engineering manager, or hybrid lead/manager for AI/ML engineering teams
• Track record of building and shipping meaningful AI systems in production or high-impact technical environments
• Strong hands-on technical ability, with comfort reviewing designs, debugging systems, and contributing directly when needed
• Experience working with LLMs, RL, agents, model evaluation, inference systems, optimization, or ML infrastructure
• Strong judgment around architecture, model behavior, evaluation, system tradeoffs, and execution priorities
• Ability to create clarity in ambiguous technical areas and help teams move quickly without losing rigor
• Experience managing or mentoring engineers while maintaining close technical involvement
• Experience partnering cross-functionally with product, research, infrastructure, and engineering leadership
• Strong ownership mindset and ability to operate in a small, high-caliber team

Desirable

• As an AI Engineering Lead, you will lead a team of engineers building systems for AI-native EDA and advanced hardware workflows
• This work sits at the intersection of applied AI, ML systems, agents, model evaluation, software engineering, and semiconductor domain complexity
• You will be responsible for setting technical direction, managing execution, developing engineers, and staying close to the implementation details that determine whether these systems work in practice
• The team is building AI systems where correctness, traceability, and reliability matter, especially when agents are operating against formal or highly structured engineering problems
• This direction maps well to the current internal work at Normal, including auto-formalizing systems for advanced hardware, scalable AI systems, ML efficiency, and AI applied to semiconductor and circuit design workflows
• Lead and manage a team of AI engineers
• Set technical direction for applied AI and ML engineering work across Normal’s product and platform areas
• Stay hands-on with architecture, implementation decisions, code review, debugging, evaluation, and system design
• Build AI systems that can operate against structured engineering workflows, formal specifications, and objective correctness signals
• Partner with product, engineering, research, and leadership to translate ambiguous goals into clear technical plans
• Help define the operating rhythm, engineering standards, and execution model for the AI team
• Hire, mentor, and develop strong AI engineers as the team scales
• Identify technical risks early and guide the team toward practical, high-quality solutions
• Balance model quality, system reliability, product impact, and engineering velocity
• Contribute directly to critical technical work when needed, especially in early or ambiguous areas