Member of Technical Staff, ML Systems

About the Role

Join a team building high-performance training and inference systems for image, video, and world-model workloads. You will improve efficiency across GPU kernels, distributed execution, and serving systems, helping make demanding AI workloads faster and more scalable.

What You'll Do

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

• Profile bottlenecks across GPU kernels, memory, systems, and clusters 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 technologies such as NCCL, RDMA, InfiniBand, and RoCE.

• Build benchmarking and regression tools to validate and preserve performance gains 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 recent, hands-on experience in ML systems or GPU performance engineering.

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

• Direct kernel development experience on 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 inference or training performance, including GPU kernels, runtimes, or distributed execution.

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

Compensation & Benefits

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

Location

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