Product Manager
Early-career Product Manager at Normal Computing working with the ASIC team to build AI tooling and an autoresearch platform for chip design — writing specs, prototyping with coding agents, shipping small code changes, and driving milestones toward tapeout. Core stack: LLM-powered products, coding agents (Claude Code, Cursor), and chip design/verification concepts.
- Compensation
- $205.5K–$225K / yr
- Location
- New York
- Work setup
- Hybrid
- Seniority
- Intern
- Employment
- Internship
- Experience
- 1+ years
- Category
- Product
- Posted
Skills
Must have
- Degree in CS, EE, physics, math, or similar, or the equivalent taught to yourself
- 1-3 years of experience in product management, engineering, research, technical program management, or a related technical role
- Can point to something that was your idea and that you made real
- Fluency with coding agents (Claude Code, Codex, Cursor, or similar)
- Experience building or evaluating LLM-powered products
- Clarity of communication in both speaking and writing
- Comfort with ambiguity — frame vague problems and come back with a proposal
- Interest in how chips are designed
Nice to have
- Coursework, research, or internship experience in chip architectures, digital design, verification, or hardware performance modeling
- Exposure to SystemVerilog
- Familiarity with the mechanisms underlying LLM inference
Full description
Normal Computing | Build with Us
Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), and London.
The Role
As an early-career Product Manager, you'll work with the Product Lead and the engineering teams to help build the AI systems and autoresearch platform our ASIC team needs to tapeout unconventional, physics-based architecture. We are rethinking the chip design system from the ground up, automating the process of research: exploring the full design space, generating candidate architectures, evaluating them at appropriate levels of fidelity, and feeding learnings from experiments run in hardware labs and deployed silicon into the next generation chip. Domains of exploration include RTL optimization, design verification, and continual learning from experiment results. You'll help decide what we build, then be accountable for delivering on the incremental milestones to tapeout.
On any given day, you might sit with a chip architect benchmarking novel architectures, shadow a verification debug session and come back with a proposal for what to automate, scope out the requirements and success criteria for a custom EDA tool, or prototype an experiment dashboard. This is a building role. You'll write specs, run experiments, and use coding agents to prototype and land fixes yourself. When you have an idea, we would rather you spend an afternoon prototyping a rough version than a week describing it.
No semiconductor background is required to start. In your first few months the bar is understanding how the team works, asking the questions that move a design discussion forward, and earning trust by following through on deliverables.
What You'll Own
- AI tooling scope: Investigate where the ASIC team's time actually goes, decide what we build, then be accountable for whether it gets used.
- Specs and prototypes: Write the requirements docs and user journeys that engineering builds from. Where a working prototype communicates faster than a document, build the prototype instead.
- Prototype to production: Work with engineers to turn a promising prototype into something the ASIC team can rely on.
- Shipping small changes: Work in the codebase with coding agents, open PRs for bug fixes or UI changes, learn from code reviews from engineers, and develop judgment about which changes are yours to make.
What Makes You a Great Fit
- A degree in CS, EE, physics, math, or similar, or the equivalent taught to yourself.