Machine Learning Compute Efficiency Lead, Infrastructure & Planning

What you'll do

• Manage ML compute resources across GPU, TPU, and custom silicon to enable large-scale model serving.
• Develop and implement resource strategies based on the roadmaps and pain points of internal engineering teams.
• Perform root cause analysis to improve performance, maximize hardware utilization, and reduce service costs.
• Architect solutions for capacity allocation, workload scheduling, and cost reduction for AI-driven experiences.
• Define and monitor compute efficiency metrics across the software engineering organization.
• Advocate for ML engineers by consolidating inference requirements for internal infrastructure providers and public cloud partners.

What we're looking for

• BS in Computer Science, Computer Engineering, or equivalent practical experience.
• MS or PhD in a relevant field is preferred.
• 7+ years of experience in ML infrastructure, systems architecture, or efficiency and optimization roles at scale.
• Strong understanding of foundation model inference, distributed training, GPU/TPU utilization, and cluster scheduling.
• Proven ability to drive complex cross-organization technical initiatives through influence.
• Experience with PyTorch, JAX, Slurm, Kubernetes, or GPU/TPU hardware.
• Experience in FinOps, capacity planning, or TCO modeling for cost optimization.
• Strong communication skills for presenting to executives and white-boarding with senior engineers.