AI Engineering Lead

Stack

Responsibilities

• ▹Lead and manage a team of AI engineers; hire, mentor, and develop engineers as the team scales
• ▹Set 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, and evaluation
• ▹Build AI systems that 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
• ▹Define the operating rhythm, engineering standards, and execution model for the AI team; identify technical risks early

Requirements

• ▹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, comfortable reviewing designs, debugging systems, and contributing directly
• ▹Experience working with LLMs, RL, agents, model evaluation, inference systems, 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

Nice to have

• ▹Experience applying agentic systems and AI to EDA, semiconductor workflows, circuits, or hardware design/verification
• ▹Experience leading AI work in a startup, research-heavy, or zero-to-one product environment
• ▹Experience hiring and scaling small, senior engineering teams

Soft skills

About the company

Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource — stochastic, in-memory, asynchronous architectures that deliver 10-100x more AI inference per dollar and per watt. The company co-designs the full stack, from AI-native EDA systems used by the world's largest semiconductor companies to the advanced ASICs they make possible. Backed by $85M+ from leading deep-tech investors, the team works across New York, Silicon Valley, London, Copenhagen, and Seoul.