Member of Technical Staff, ML Systems

About the Role

Join an AI and machine learning team building high-performance training and inference systems for image, video, and world-model workloads. You will improve performance across the stack, from GPU kernels to distributed training and serving.

What You'll Do

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

• Profile 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 benchmarks and regression tests to preserve performance gains in production.

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

What We're Looking For

• At least 2 years of experience in deep learning training or inference systems, GPU performance, or distributed systems.

• Hands-on ML systems or GPU performance experience in a recent role, including work on inference or training performance.

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

• Direct experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX.

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

• Experience optimizing 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.