Software Engineer, GPU Infrastructure
Fluidstack3 months ago
Base Salary
$208k - $269k/yr
Responsibilities
- Own compute fleet health end to end through metrics pipelines, alerting, and unified health views across Kubernetes-orchestrated workloads and bare metal.
- Build automation for compute failure detection, triage, parts management, repair, and return to service.
- Design and expand GPU qualification platforms covering burn-in, performance baselining, and new product introduction execution.
- Own Redfish and BMC tooling for firmware-level telemetry, log collection, and low-level fleet access.
- Drive reliability, scalability, observability, incident response, and operational automation for a hyperscale GPU fleet.
- Carry a pager, run incidents, write postmortems, and fix systemic causes.
- Use AI tooling and coding assistants to build production automation in the language best suited to the problem.
Requirements
- Experience shipping production automation that other teams depend on.
- Comfort reasoning about hardware failure modes at the firmware and silicon level.
- Ability to operate effectively in ambiguous, unfamiliar technical domains and learn quickly.
- Willingness to participate in on-call incident response and postmortem-driven improvement.
- Fluency with AI tooling, including LLM APIs, MCP servers, agentic frameworks, Claude Code, Cursor, or similar tools.
- Bonus experience with hardware lifecycle management, RMA automation, BMC or Redfish tooling, GPU qualification or burn-in frameworks, workflow orchestration engines, metrics and alerting pipelines, Go, or Python.
Benefits
- Competitive total compensation package including salary and equity.
- Retirement or pension plan in line with local norms.
- Health, dental, and vision insurance.
- Generous paid time off policy in line with local norms.
- Equity may be provided in the form of restricted stock units.
- The posting states a commitment to pay equity and transparency.
Tech Stack
Categories
DevOpsSite Reliability
About Fluidstack
Fluidstack provides on-demand GPU cloud infrastructure for AI training and inference, offering dedicated clusters of NVIDIA H200 and GB200 systems. The company sells compute capacity and managed clusters to AI labs, enterprises, and public-sector teams that need high-throughput workloads. Founded in 2017 and headquartered in New York, it is privately held.