Member of Technical Staff, ML Systems

About the Role

Join an AI/ML team building and improving the training and inference stack for image, video, and world-model workloads. You will own performance across GPU kernels, distributed systems, and serving, helping make demanding models faster and more efficient.

What You'll Do

• Optimize training and inference performance for image, video, and world-model workloads.

• Profile and remove bottlenecks at the kernel, memory, system, and cluster levels using Nsight and related tools.

• Implement low-level optimizations with CUDA and Triton.

• Design and improve distributed training and inference engines for diffusion models.

• Improve communication performance across GPUs and nodes using NCCL, RDMA, InfiniBand, RoCE, and disaggregated serving approaches.

• Build benchmarking and regression tools to ensure performance improvements persist in production.

• Co-design kernels, runtimes, and models with target hardware in mind.

What We're Looking For

• At least 2 years of experience in deep learning training or inference systems, distributed systems, or related ML systems work.

• At least 1 year of hands-on ML systems or GPU performance experience in your current or most recent role.

• Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, Megatron, or a comparable system.

• Hands-on experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX. Kernel experience must include direct kernel development.

• Strong machine learning and computer science fundamentals, with a degree in computer science or a related quantitative field.

• Experience optimizing inference or training performance across GPU kernels, runtimes, or distributed execution.

• Experience with diffusion, video, image, or other multimodal workloads is valuable.

Compensation & Benefits

Base salary is $180,000 to $260,000 annually. Visa sponsorship is available.

Location

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