ML Infrastructure Engineer, ML Compute Capacity

What you'll do

• Build and operate demand and capacity planning systems for large-scale accelerator fleets.
• Develop data pipelines and telemetry systems to ingest and serve fleet-wide utilization and cost data.
• Create observability infrastructure including monitoring, alerting, and dashboards for real-time fleet health.
• Drive innovation in forecasting, optimization, and supply chain management tooling at scale.
• Build end-to-end tools from data models and APIs to interactive dashboards for leadership insights.
• Develop self-service platforms with defined schema contracts to balance usability, utilization, and costs.
• Manage the distribution of compute resources across multi-tenant and heterogeneous hardware environments.

What we're looking for

• 7+ years of experience in relevant areas.
• Experience with machine learning infrastructure on GPUs or TPUs.
• Proficiency in Python and/or Go for production backend and data engineering work.
• Experience building data pipelines and crafting robust queries over large-scale, multi-source data.
• Experience with observability tools like Prometheus, Grafana, or equivalent monitoring systems.
• Strong CS fundamentals and excellent problem-framing and problem-solving skills.
• Bachelor's degree or higher in Engineering, Mathematics, Economics, or a related quantitative field.
• Experience with Kubernetes at production scale, modern web frameworks like React, or familiarity with accelerator utilization patterns (preferred).