FluidStack
Software Engineer, Energy Management
About this role
Join Fluidstack's Facility Controls team to design and build production energy management systems for gigawatt-scale AI data centers. You'll own the real-time control infrastructure that manages power delivery, integration with facility systems, and autonomous deployment across cutting-edge compute facilities.
What you'll do
- Build and maintain production facility energy control services including protocol layers, gateway services, and APIs for third-party controllers
- Design the messaging and state layer that computes facility load intent from live telemetry and publishes forecasts in real-time
- Own data contracts and engineering standards ensuring consistency across multiple sites and counterparties
- Implement constraint execution logic to determine facility capabilities and delivery timelines within sub-second deadlines
- Build observability and monitoring systems to track latency, data staleness, and system health across the production stack
- Drive production incident response and ensure system reliability at scale
What they're looking for
- High-throughput messaging systems (NATS, Kafka, or equivalent) at scale
- Time-series database design (ClickHouse, TimescaleDB)
- Production observability and monitoring (Prometheus, Grafana)
- Go programming language
- Production service architecture and design patterns
- Data integrity and signal chain management (device to database)
- Industrial/utility protocols (DNP3, Modbus TCP, OPC UA) — bonus
- Real-time control systems and SCADA/EMS experience — bonus
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FluidStack
FluidStack builds AI infrastructure at scale, developing data centers and warehouse operations designed to handle gigawatt-capacity compute deployment. The company is hiring for warehouse engineers, data center operations specialists, product engineers, and people leaders to support rapid infrastructure expansion across multiple sites.
- Website
- fluidstack.io
Likely interview questions
- Describe a high-throughput messaging system you've deployed to production and the failure modes you discovered or prevented.
- Walk us through how you've designed a time-series data model; what tradeoffs between write throughput and query performance did you make and why?