Senior Product Manager

Instrumental, Inc.•Palo Alto, CA

9d•$174,000 - $227,000

About The Position

Instrumental builds technology that enables electronics manufacturing to improve itself. Our Manufacturing Acceleration Platform combines factory signals, performance data, and deep context to improve yield and throughput. NVIDIA, Meta, and L3Harris rely on Instrumental to accelerate product development and scale manufacturing. Our customers represent 80% of the AI infrastructure market, building the PCBAs, trays, and racks used in AI datacenters. Our customers are engineers solving difficult problems in how physical products are built. Earning their trust takes close relationships, technical depth, and products whose recommendations withstand scrutiny. As a Senior AI Product Manager at Instrumental, you will work directly with these engineers to understand their workflow, turn their expertise into product capabilities, and measure the value those capabilities deliver. Instrumental’s product spans AI software, vision hardware, and forward-deployed services – and your solutions will integrate all three to deliver outcomes to customer manufacturing lines. AI changes how we do that work. Customer discovery, design, and building happen together. As building gets easier, choosing what to focus on, defining what good looks like, and learning from real use is the only thing that matters. You’ll develop and leverage deep customer expertise, and collaborate extensively with our own customer-facing teams, internal experts, design, and engineering to deliver. We evaluate ourselves on building the right product, fast and well.

Requirements

Real zero-to-one experience: 5+ years of hands-on product management or founder experience at an early- or mid-stage startup with significant ownership of a product offering. You have real startup DNA and thrive in high-risk, uncertain environments.
Radical attention on the customer. Every PM knows they should spend time with customers, but we only want to talk to those who carve out 20%+ time week after week to make customers a core part of their product practice.
Trusted by technical customers: demonstrated ability to earn the trust of technical customer stakeholders. Manufacturing or industrial AI experience is not required because you’ve shown you can quickly learn unfamiliar domains.
Relentless about data. Demonstrated ability to understand complex customer datasets. The core of Instrumental is manufacturing data from some of the world’s most complex devices – you are a whiz with spreadsheet spelunking, basic statistics, and database queries.
Proven product taste. In the AI Era, taste and human-centric storytelling is in short supply. You love AI tools but reject AI slop in favor of thoughtful, sound judgment to drive product prioritization and build conviction.
Leading edge AI fluency. You’re constantly evolving your product practice to incorporate new tooling and the new ways of working they enable. You operate at the frontier of AI-driven collaboration and productivity, and help everyone on your team make better decisions, understand customers, and do useful work.

Nice To Haves

You’re encouraged to apply even if your experience doesn’t precisely match the job description!

Responsibilities

Know the customer: Investigate workflows, equipment, data, and constraints with customer engineers; incorporate your learnings into your prioritization and storytelling. Travel up to 30%.
Deliver on business objectives: Set clear targets for product performance, market fit and value, and consistently drive towards them.
Work in public: Make priorities, trade-offs, success criteria, roadmaps, and decisions visible across the organization.
Prototype and simplify: Prototype with AI and run field trials. Test assumptions, reduce complexity, and deliver useful improvements quickly.
Define quality and trust: Build evals and context with domain experts, check automated judgments against theirs, and validate under real operating conditions. Design how users verify, correct, and recover from outputs, including when human approval is required.
Enable independent creation: Build reusable guidance and product-work harnesses with clear decision boundaries and maintenance ownership. Turn field learning into repeatable capabilities.
Follow through: Track outcomes, resolve deployment barriers with the relevant teams, and turn failures into tested improvements. Close the loop with contributors.