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