ML Compute Efficiency Automation Engineer, Infrastructure & Planning

What you'll do

• Build systems of record for resource requests, allocations, and utilization to ensure accurate data at scale.
• Identify and resolve inefficiencies in inference and training workloads across GPUs, TPUs, and Apple Silicon.
• Solve core optimization problems regarding scheduling, capacity allocation, and serving costs for large models.
• Automate manual workflows such as triage, reporting, and reconciliation to eliminate operational toil.
• Develop telemetry, schemas, and anomaly detection systems to surface efficiency and cost opportunities.
• Re-architect infrastructure processes that buckle under scale to ensure they grow with usage rather than headcount.
• Create reusable tools and automated systems that allow the team to operate without manual intervention.

What we're looking for

• BS in Computer Science, Computer Engineering, or equivalent practical experience.
• 6 or more years of experience building production software, automation, tooling, or data and infrastructure systems.
• Proficiency in Python or similar languages for automation, pipelines, and tooling.
• Experience with SQL and creating dashboards or data products in tools like Tableau, Looker, or Grafana.
• Experience designing data models or telemetry schemas for infrastructure, capacity, or utilization data.
• Experience operating complex systems in large-scale compute, cloud, or infrastructure environments.
• Proficiency with AI tooling and coding assistants as part of a standard workflow.
• Experience with ML training/inference infrastructure, GPU/TPU utilization, and foundation model serving.