USAMember of the Technical Staff - Product
1 мес. назад
USALeadRemote
apibillinginvoicingmonitoringdata-management
Engineering role responsible for developing and owning backend systems that manage compute capacity as a product, including APIs, billing, invoicing, and dashboards.
Обязанности
- We are looking for engineers who can reason about our entire system, from the machine to the invoice, and own the layer where it becomes a product.
- The system of record where capacity is quoted, reserved, funded, and billed, on every side of the market
- The full API surface that connects and operationalizes our infrastructure
- Money paths: metering, billing, invoicing, settlement
- Self-serve, from signup to a running workload
- Dashboards for customers, providers, and operators
- Participating in on-call rotations
Требования
- Impressive technical work you can go deep on, with impact in the world. That can take three years or twenty.
- Comfortable working across the stack: Go, Python, and TypeScript
- Strong grasp of transactional correctness.
- API design experience: OpenAPI, versioning, backward compatibility
- Experience building alongside Kubernetes or bare-metal infrastructure
- Experience with at least one of: billing and payments, marketplaces, or multi-tenant access systems
- Strong product sense and experience driving product outcomes
- Cluster is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Другое
- Location: North America Remote / San Francisco, CA · Full-Time
- Compute is the most sought-after resource in the world, yet it still trades like commercial real estate: year-long contracts, manual fulfillment, capacity sitting idle because nobody can move it. We are building the liquidity layer at .
- was founded by Nat Friedman and Daniel Gross to give startups the scaled AI infrastructure once reserved for hyperscalers. The first cluster filled almost instantly. The years since went into the platform that makes compute liquid: it deploys into foreign datacenters and turns the hardware it finds into clusters that leading AI labs train on.
- Today operates compute for 80+ customers across 30+ capacity providers, with tens of thousands of GPUs under management and billions of GPU-hours supported, on everything from A100 to GB300.