ML Compute Efficiency Automation Engineer, Infrastructure & Planning - Apple

Key Responsibilities

• Design and implement automated pipelines that optimize ML model training and inference workloads on shared infrastructure.
• Collaborate with ML teams to profile models, identify bottlenecks, and apply compute‑efficiency techniques such as mixed‑precision, model pruning, and dynamic batching.
• Build and maintain scalable, cloud‑native infrastructure using AWS services, Kubernetes, and IaC tools to support high‑throughput ML workloads.
• Develop monitoring and alerting solutions to track resource utilization, latency, and cost metrics across the ML ecosystem.
• Drive continuous improvement initiatives, documenting best practices and contributing to platform tooling that reduces operational friction for software engineers.

Requirements

• 5+ years of experience in ML infrastructure or DevOps roles, with a strong focus on compute efficiency.
• Proficiency in Python, AWS, Kubernetes, and infrastructure automation (Terraform, Helm).
• Hands‑on experience with ML profiling, model optimization, and performance tuning.
• Excellent problem‑solving skills and ability to work cross‑functionally with ML, platform, and software engineering teams.
• Strong communication skills and a track record of delivering production‑ready tooling.