# Software Engineer, Facility Software Automation

**Company:** [Fluidstack](https://hotfix.jobs/companies/fluidstack)
**Location:** Austin, TX, New York, NY, San Francisco, CA, Seattle, WA
**Role:** Fullstack Engineering
**Salary:** $200k – $250k/yr
**Experience:** 5+ years
**Skills:** Kafka, Nats, ClickHouse, Timescaledb, Prometheus, Grafana, Kubernetes, Argo CD
**Posted:** 2026-07-06

> Build and own production telemetry, messaging, time-series data, and automation services for critical infrastructure at gigawatt-scale AI compute facilities. Requires ownership of high-throughput pipelines, data integrity, and observability from device to dashboard.

## Job Description

## Responsibilities
- Build and own production services across the telemetry collectors, ingesters, and automation stack that every downstream Automation and Operations team depends on.
- Design and evolve the messaging and time-series data layer that ingests real-time telemetry from device signals across the global compute fleet.
- Own the software infrastructure and data lake underpinning the platform, setting the data contracts and engineering standards other teams build against.
- Build the APIs and dashboards that surface telemetry to commissioning, operations, and leadership teams in real time.
- Drive the integration between device-level data streams and the automation API layer as the platform scales to new sites.
- Move toward a broken pipeline in production with urgency and without drama.

## Requirements
- Built and owned production services that other teams depend on, and don't ship code you wouldn't want to be paged for at 3am.
- Treat telemetry pipeline health and data integrity as non-negotiable.
- Worked with high-throughput messaging systems (NATS, Kafka, or equivalent) at real scale and know where the failure modes show up before they hit production.
- Designed time-series data models in ClickHouse, TimescaleDB, or a comparable system, and can walk through the tradeoffs between write throughput, query performance, and schema evolution.
- Instrumented production services with observability tooling (Prometheus, Grafana, or equivalent) and treat metrics and alerting as part of shipping, not an afterthought.
- Worked across the full signal chain from device to database, and design pipelines backwards from device-level constraints so they're correct by design.

## Nice-to-Haves
- Kubernetes and ArgoCD for production service deployment.
- ClickHouse schema design for time-series workloads.
- Compute or critical infrastructure telemetry environments.
- Real-time dashboarding with Grafana or equivalent.

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