ML Infrastructure Engineer - ML Compute Capacity

What you'll do

• Build demand and capacity planning systems, data pipelines, telemetry, and observability infrastructure.
• Develop forecasting, optimization, and supply-chain tools, plus APIs and dashboards that turn fleet data into actionable insights.
• Build self-service platforms and collaborate with finance, supply chain, data center operations, and infrastructure teams.

What they ask for

• Experience with machine-learning infrastructure on GPUs or TPUs.
• Proficiency in Python and/or Go for production backend and data engineering.
• Experience building data pipelines and querying large-scale, multi-source data; experience with observability or equivalent monitoring systems.
• Bachelor's degree or higher in Engineering, Mathematics, Economics, or a related quantitative field; strong CS fundamentals and problem-solving skills.

Nice to have

• Production-scale Kubernetes operations; experience with modern web frameworks such as React.
• Familiarity with accelerator utilization patterns and interest in capacity planning, cost attribution, or FinOps systems.

Pay

USD $184,700 to $324,800 base salary

Tools

GPUs, TPUs, Python, Go, Trino, PostgreSQL, Elasticsearch, Prometheus

AI focus

Infrastructure for large-scale ML training and inference, including accelerator capacity planning, utilization telemetry, forecasting, and optimization.