AI Engineering Lead

Location

New York City; London; Palo Alto (Silicon Valley)

Employment Type

Full time

Location Type

Hybrid

Department

Engineering

Compensation

• $250K – $340K
• Offers Equity

We are committed to competitive and equitable compensation based on role, skills, and experience. Salary ranges are guidelines, with final compensation varying by role, experience, and location and reviewed regularly for fairness.

OverviewApplication

About Normal Computing Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt.

We co-design the full stack: AI-native EDA systems in production with the world’s largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world’s leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing.

Normal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority.

The Role As our 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 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.

What You’ll Own

• Team Leadership: Lead and manage a team of AI engineers; hire, mentor, and develop strong engineers as the team scales.
• Technical Direction: Set direction for applied AI and ML engineering work across Normal’s product and platform areas.
• Hands-On Depth: Stay hands-on with architecture, implementation decisions, code review, debugging, evaluation, and system design; contribute directly to critical technical work when needed.
• Structured AI Systems: Build AI systems that operate against structured engineering workflows, formal specifications, and objective correctness signals.
• Cross-Functional Translation: Partner with product, engineering, research, and leadership to translate ambiguous goals into clear technical plans.
• Execution Model: Help define the operating rhythm, engineering standards, and execution model for the AI team; identify technical risks early and balance model quality, reliability, product impact, and velocity.

What Makes You a Great Fit

• Direct experience across AI engineering, ML engineering, applied AI, production ML systems, or AI infrastructure
• 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
• 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
• Strong ownership mindset and ability to operate in a small, high-caliber team

Bonus Points

• Experience applying agentic systems and AI to EDA, semiconductor workflows, circuits, hardware design, verification, or other advanced enginee…