ML Performance Engineer

Role description

Role Overview

Join our MLOps team as an ML Performance Engineer to validate new clusters, improve managed Kubernetes and Slurm platforms, and produce performance benchmarks for customers and the broader market.

What You Will Do

Validate new cluster deployments, identify and diagnose performance and reliability issues, and run model benchmarks across training and inference.

Why It Might Be a Fit

Required qualifications include a Bachelor of Science in Computer Science or a related technical field, 3+ years of experience running ML workloads on GPU clusters, and hands-on experience with distributed training using frameworks such as PyTorch FSDP.

Requirements

• Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
• 3+ years of experience running ML workloads on GPU clusters
• Hands-on experience with distributed training using frameworks such as PyTorch FSDP, Megatron-LM, DeepSpeed, or torchtitan
• Working knowledge of Slurm and Kubernetes from a user and workload perspective
• Experience profiling and optimizing GPU workloads, including collective communication performance
• Strong Python skills and solid Linux fundamentals
• Clear written communication of performance findings to engineering and non-technical audiences

Benefits

• Stock Options
• 100% paid Medical, Dental, and Vision insurance for Employees
• Company Health Savings Account Contributions
• 100% paid Short Term and Long Term Disability Insurance for Employees
• Life and Voluntary Supplemental Insurance Options
• Other Insurance Options, such as Pet & Legal Insurance
• Various Supplementary Health Benefits, such as discounted Virtual Healthcare Appointments and Serious Illness Support
• Flexible Spending Account
• 401(k)
• Employee Assistance Program
• Flexible PTO
• Paid Holidays
• Parental Leave
• Other In-Office Perks