ML Compute Efficiency Automation Engineer, Infrastructure & Planning

What you'll do

• Automate resource requests, allocation tracking, escalations, reporting, and other operational workflows; build systems of record and reusable tools.
• Analyze training and inference workloads to identify wasted compute and help improve scheduling, capacity allocation, utilization, and serving cost.
• Build telemetry, data models, dashboards, and anomaly detection that support trustworthy decisions and automated action.

What they ask for

• BS in Computer Science, Computer Engineering, or equivalent practical experience.
• 6 or more years building production software, automation, tooling, or data and infrastructure systems.
• Strong programming skills in Python or similar; automation, pipelines, and tooling; SQL plus dashboards or data products.
• Experience designing infrastructure, capacity, or utilization data models or telemetry schemas, and running complex systems in a large-scale compute, cloud, or infrastructure environment.

Nice to have

• Production experience shipping automated or autonomous workflows; automated alerting or anomaly detection for infrastructure metrics.
• Understanding of ML training and inference infrastructure, or experience with FinOps, capacity planning, cloud cost management, or IT governance; knowledge of Django/Postgres.

Pay

$184,700 and $324,800 (base salary)

Tools

Python, SQL, Tableau, Looker, Grafana, Django, Postgres, GPUs, TPUs, and custom Apple Silicon

AI focus

Build AI automation that replaces manual operations with self-running, self-correcting workflows; use coding assistants as part of daily work. The role also supports ML compute efficiency for training, inference, and foundation model serving.