Machine Learning Systems Engineer

About the company

Our client is a fast-growing technology company.

The role

Raydar is recruiting for this role on behalf of our client. Own speed and efficiency for machine learning training and serving systems, working from low-level GPU code up to multi-machine deployments. You will profile, tune and harden these systems alongside a small, senior technical group.

What you'll do

• Improve GPU and system performance for model training and serving.
• Find and resolve slowdowns across the hardware, memory, software and cluster layers.
• Write low-level GPU optimizations using CUDA and Triton.
• Design and improve distributed training and serving engines.
• Own communication performance between GPUs and machines, including high-speed networking and disaggregated serving.
• Build benchmarking and regression tooling so performance improvements hold up in production.
• Co-design kernels, runtimes and models with the underlying hardware in mind.

Requirements

What we're looking for

• Hands-on experience writing custom GPU kernels for NVIDIA hardware.
• Has built core capabilities inside a widely used model training or serving framework, rather than only deploying or integrating it.
• Degree in computer science or a related quantitative field; recent graduates should come from a top program.
• A year or more of current, professional experience in machine learning infrastructure or GPU tuning.
• Track record of speeding up model execution at the hardware, runtime and multi-machine levels.
• Strong grounding in machine learning and computer science fundamentals.

Bonus points

• Background tuning generative models that work with visual or other non-text data.

Benefits

Compensation and benefits

• Base salary: USD 180,000 to 230,000 per year
• Equity

Location and work model

• California, United States
• On-site, 5 days a week in the office
• Full-time