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