The Compute Production TeamThe Compute Production team builds the data foundations and internal products that let Fluidstack bring gigawatt-scale AI compute online and keep it running.Examples of key problems the team is working on:
- Build the ontology that models compute production end to end, from hardware and networks to the operational workflows that deliver clusters to customers
- Turn fragmented operational data spread across tools, spreadsheets, and people's heads into a single source of truth engineers and operators can build against
- Ship product surfaces that give compute production teams real leverage: fewer manual handoffs, faster diagnosis, clearer state of the fleet
- Apply LLM-era tooling to operational workflows where structured data and automation can replace repetitive human effort
Role Scope- Own product direction for the team's ontology and data products serving compute production: the roadmap, the priorities, and the success metrics that justify them
- Turn messy compute production domains into data models, specs, and product surfaces clear enough that engineers can ship against them without a second round of discovery
- Drive technical tradeoffs with engineers and ship end to end, from problem definition through delivery
- Run direct discovery with compute production stakeholders to understand their domain, then validate that what you built actually changes how they work
- Define and track the metrics that prove the product is delivering real leverage, and cut or redirect work that isn't
What We're Looking ForThe below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.- 3+ years of technical product management, ideally on data, platform, or ontology products
- A track record of shipping products with measurable impact, and the numbers to show it
- You can reason about system design and hold your own with engineers on technical tradeoffs, not just relay their conclusions
- Strong written communication: crisp specs, and prioritization rationale sharp enough that stakeholders who disagree still understand why you chose what you chose
- You're comfortable in ambiguity and know how to impose structure where none exists, turning an unmapped operational domain into a model others can build on
- Bonus: exposure to compute or datacenter operations, infrastructure systems, or building LLM-powered applications
We are committed to pay equity and transparency.
