AI Engineering Lead

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.

As an AI Lead / ML Engineering Manager, you will lead a team of AI/ML engineers building systems for AI-native EDA and advanced hardware workflows. This work sits at the intersection of applied ML, 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 our 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 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/ML systems in technical domains where models need to operate against real constraints. Experience with LLMs, RL, agents, ML infrastructure, optimization, model evaluation, or AI applied to hardware, EDA, circuits, or engineering workflows is especially relevant.

This direction maps well to the current internal signal at Normal, including work around 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/ML 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/ML team

• Hire, mentor, and develop strong AI/ML 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

• Direct experience across ML engineering, applied AI, AI infrastructure, production ML systems, 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/ML 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

• Experience applying agentic systems and AI/ML to EDA, semiconductor workflows, circuits, hardware design, verification, or other advanced engineering domains

• Experience leading AI/ML work in a startup, research‑heavy, or zero‑to‑one product environment

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