Backend Engineer

Fluidstack•Austin, TX

1d•$175,000 - $300,000

About The Position

Fluidstack is building civilization-scale infrastructure for AI, aiming to deliver 10 to 100s of GWs of compute faster than anyone else. This involves rethinking every layer of the stack, from acquiring power and designing data centers to operating them. The company values extreme ownership, velocity, first principles thinking, and a passion for the problem space. The Production Engineering Team is at the forefront of this mission, tackling complex challenges such as making tens of thousands of GPUs legible in real-time through an observability platform, building a unified control plane for infrastructure management, and ensuring the system's view of itself always matches reality. This role offers the opportunity to own critical systems and contribute to a rapidly scaling, high-impact organization.

Requirements

Treat toil as a bug; build solutions to eliminate repetitive human tasks.
Design APIs that age well, avoiding leaky abstractions at scale.
Move toward ambiguity, build maps, and explain them.
Learn at a steep slope, reaching competence in unfamiliar domains quickly.
Carry a pager without flinching, run incidents, write postmortems, and fix systemic causes.
Be fluent with AI tooling such as LLM APIs, MCP servers, and agentic frameworks, and drive daily use of tools like Claude Code or Cursor.
Have shipped production services that other teams depend on at scale.
Be comfortable in any language using AI coding tools.

Nice To Haves

Distributed systems and data pipeline engineering.
Experience with time-series observability stacks (Prometheus, Thanos, VictoriaMetrics).
API design and versioning at scale.
Experience with workflow and orchestration engines (Temporal, Cadence).
Familiarity with BMC/Redfish or hardware telemetry.
Proficiency in Go, Python, and Postgres.

Responsibilities

Own the observability platform, building and operating data pipelines, the decoration and correlation engine, and the healthcheck framework to make the fleet legible from site down to device and link.
Define and build the API surface for infrastructure, designing contracts between production infrastructure and all tools that interact with it.
Build the production control plane, encompassing unified machine management, actual state inspection, distributed command execution, and the underlying Kubernetes infrastructure.
Own fleet state as the source of truth, including SLOs, site lifecycle state, and integration with internal infrastructure management and customer-facing operations platforms.
Ensure new hardware is landed cleanly into the platform, managing ZTP, DHCP, DNS, and artifacts for new XPU generations and site integrations through IaaS before production.