Machine Learning Systems Engineer

The RoleRaydar 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 DoImprove 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.What We're Looking ForHands-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 PointsBackground tuning generative models that work with visual or other non-text data.Location And Work ModelCalifornia, United StatesOn-site, 5 days a week in the officeFull-timeCompensation, As The Posting Describes ItEquity