AI Hiring Index

Fluidstack · Product & Design · Unspecified · Posted 2026-09-10

Product Manager, Compute Operations

Fluidstack · New York, NY; San Francisco, CA; Austin, TX; Seattle, WA · $245k–300k base

This range's midpoint is above 81% of posted product & design ranges at AI companies right now. See the salary index.

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ABOUT FLUIDSTACK

We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it.

We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI.

We hire people who care deeply about this problem space. If that is you, please apply!

HOW WE OPERATE

- Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.

- Insane urgency. We drive everything forward as fast as possible.

- Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.

- Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.

- Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.

THE DECISION TEAM

Examples of key problems the team is working on

- Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.

- Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.

- Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.

ROLE SCOPE

- Own the automation roadmap for compute production as its first product manager: you decide which parts of keeping GPU fleets worth billions healthy become software next, across fleet health, repair and RMA, hardware qualification, on-call, facility maintenance, and the asset model, and you defend the order with numbers: machines per operator, time to return to service, pages per failure mode.

- Land the systems already in flight: a maintenance system for lockout tagout and work orders is live at one site and rolls out to two more, every asset register loads before the first external audit this fall, and the legacy datacenter inventory retires before the next building energizes. Your first quarter is sequencing those three and calling the cutover dates.

- Live on the floor and the rotation: embed with the production engineers and facility operators who run the fleet, sit the on-call shift, map where shift hours actually go, and turn that map into the roadmap everyone can cite, including the SOP source of truth and training records a hyperscaler customer asked to audit.

- Define done for every workflow: the number each automation must clear before it counts, published where the team sees its own toil falling, plus the site SLO and deployment cycle time dashboards the customer reads, so "did it work" is answered by the system, not a status meeting.

- Run the queue for the four Decision Engineers hired into this function: there is no team lead between you and them. You own what ships next and why, they own how, you prototype your own ideas with AI tools (Claude Code, Cursor, LLM APIs, MCP) and put working software into a shift lead's hands instead of writing requirements, …

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