Senior Member of Technical Staff, ML Systems

Help build and optimize the training and inference systems behind image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and multi-node serving, working with a team focused on making advanced models faster and more efficient.

Optimize training and inference performance for image, video, diffusion, and world-model workloads. Profile bottlenecks at the kernel, memory, system, and cluster levels using Nsight and related tools. Implement low-level GPU optimizations using CUDA and Triton. Design and improve distributed training and inference engines for diffusion models. Improve communication performance across GPUs and nodes using NCCL, RDMA, Infini Band, or RoCE. Build benchmarks and regression tests to preserve performance gains in production.

Contribute to hardware-aware kernel, runtime, and model co-design.

At least two years of experience in deep learning training or inference systems, distributed systems, or related work, including at least one year of hands-on ML systems or GPU performance work in your current or most recent role. A degree in computer science or a related quantitative field, with strong computer science and machine learning fundamentals. New graduates should have a strong academic background.

Direct experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX. Authorship of core features in an inference or training framework, rather than deployment or integration work alone. Experience improving inference or training performance across kernels, runtimes, or distributed execution. Experience optimizing diffusion, video, image, or other multimodal workloads is valuable. Primary experience in GPU-based ML systems, rather than AMD, FPGA, or custom-accelerator work.

Compensation & Benefits Base salary is $180,000 to $230,000 USD annually. Visa sponsorship is available.

On-site in Menlo Park, California, United States.