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Salary: USD 250,000 - 300,000 / annual
Crusoe is building the operating system for the AI datacenter—a unified platform that senses, reasons about, and acts on one of the world's largest managed GPU fleets. The company operates a vertically integrated AI infrastructure stack from electrons to tokens, and is solving the energy bottleneck in AI compute through an energy-first approach.
You will work on the control plane for this massive fleet at a critical inflection point where fleet autonomy remains an open problem at scale. This is core distributed-systems engineering: designing and building the platform services, APIs, SDKs, and developer experiences that enable fleet engineers, SREs, and product teams across Crusoe to operate and extend the infrastructure themselves.
The platform consists of four integrated layers: agents on every site and host collecting telemetry and executing commands; a distributed infrastructure graph modeling system connections down to rack, fabric, power, and cooling; a reconciliation core with workflow, policy, and state-reconciliation engines; and domain services spanning provisioning, firmware upgrades, validation, deployment, repair, capacity, power/thermal, and Day-2 operations. Everything ships as a product with versioned APIs, SDKs, workflow templates, and a self-serve developer portal.
Key responsibilities include:
- Design and build core platform services: RBAC, tenancy, workflow engine, policy engine, and state reconciler driving fleet actions safely at scale
- Design the public API gateway, resource model, and versioned API contracts that internal teams build against
- Build SDKs, workflow templates, golden paths, and the developer portal with micro frontend framework
- Build the inventory and topology graph as the fleet's source of truth, with pipelines keeping it honest against reality
- Build site, GPU, and network agents and the event bus moving fleet telemetry and commands reliably
- Deliver the platform roadmap: pilot site, first site deployed entirely through platform, zero-downtime firmware upgrades, first fully automated RMA, then 100K+ GPUs with MTTD under 60 seconds and MTTR under 30 minutes
- Partner with embedded engineers from fleet and production engineering to turn operational insights into extensible services
You will measure success by what stops paging humans and how fast other teams ship on your platform. This role requires treating internal engineers as customers, sweating API ergonomics and documentation, and owning hard abstractions that ten teams build on top of. The interesting problems live where physical infrastructure meets software: a firmware counter, thermal event, and scheduling decision are one problem, not three.
REQUIREMENTS:
- 10+ years building distributed systems, control planes, or infrastructure platforms
- Strong software engineering skills in Go, Python, or Rust
- Experience building platforms other engineers consume: public or internal APIs, SDKs, or developer tooling with real adoption
- Depth in at least one of: workflow/orchestration engines (Temporal or similar), event-driven architectures, graph data models, policy/rules engines, or reconciliation-based control loops (Kubernetes operator patterns)
- Experience running what you build: you have carried a pager for a platform other teams depend on
- Systems thinking across the hardware/software boundary
BONUS EXPERIENCE:
- Internal developer platforms: API gateways, service catalogs, Backstage-style portals, micro frontend architectures
- GPU or bare-metal fleet infrastructure: DCGM, Redfish/IPMI, firmware lifecycle
- High-cardinality observability platforms (per-GPU telemetry at fleet scale)
- InfiniBand or RoCE fabrics
- AI agents applied to infrastructure triage and autonomous remediation
