Job 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. - 1-3 years of experience in product management, engineering, research, technical program management, or a related technical role. - You can point to something that was your idea and that you made real. - Fluency with coding agents. You already use Claude Code, Codex, Cursor, or something similar as a normal part of how you work. - Experience building or evaluating LLM-powered products. - Clarity of communication in both speaking and writing. - Comfort with ambiguity. Much of what you work on will be underspecified. You should be able to take a vague problem, frame it, and come back with a proposal. - Interest in how chips are designed. BONUS POINTS - 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 Equal Employment Opportunity Statement Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status. Accessibility Accommodations Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com. Privacy Notice By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Applicant Privacy Notice normalcomputing.com.

Requirements

Must-haves

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.
A 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.
You can point to something that was your idea and that you made real.
Fluency with coding agents. You already use Claude Code, Codex, Cursor, or something similar as a normal part of how you work.
Experience building or evaluating LLM-powered products.
Clarity of communication in both speaking and writing.
Comfort with ambiguity. Much of what you work on will be underspecified. You should be able to take a vague problem, frame it, and come back with a proposal.
Interest in how chips are designed.

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aidesign systemllmaccessibility

Platform: ashby
Effort: quick