Decision Engineer, Compute Operations
Fluidstack, New York, NY +3
- Pay
- $224K – $300K • Offers Equity
- Work type
- Onsite
- Level
- Not stated
- Area
- Infra, platform & SRE
- Posted
- Sep 10, 2026
About the role
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
- Build incident management for physical assets: related alarms roll up into a single incident that reaches on-call with a probable cause already drafted, so work goes out the moment an alert lands. Root cause analysis traces every failure to the asset behind it, and on-call rotations run from the same system.
- Close the loop from failure to return to service: failure thresholds route machines to repair automatically. Each one is triaged, a spare is pulled from the warehouse and scanned at the swap, and the broken part goes back to the vendor on an RMA.
- Sequence and staff every job: each job runs as ordered steps, and the system checks one step is done before the next starts. Shift scheduling finds who is available, and each production engineer's shift todo list is automatically generated for them.
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Listed on Fluidstack's Ashby job board; checked 23 h ago. First seen by us 25 h ago. Being listed doesn't mean the team is actively interviewing. We found this board in a public directory and haven't yet confirmed it against the company's own careers page.