Member of Technical Staff, ML Systems

About the Role

Join a technical team building the training and inference stack for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and serving systems, 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 bottlenecks at the kernel, memory, system, and cluster levels using Nsight and related tools.

• Implement low-level GPU 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, or RoCE.

• Build benchmarking and regression tools to track performance gains in production.

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

What We're Looking For

• Two or more years of experience in deep learning training or inference systems, distributed systems, or related ML systems work, including hands-on performance work.

• Experience authoring core features in an inference or training framework, rather than focusing primarily on deployment or integration.

• Hands-on GPU kernel development for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX.

• Experience with multi-GPU or multi-node communication technologies such as NCCL, RDMA, InfiniBand, or RoCE.

• Strong machine learning and computer science fundamentals, and a degree in computer science or a related quantitative field. New graduates should have strong academic credentials.

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

• Primary experience should be in NVIDIA GPU systems, rather than AMD, FPGA, or custom-accelerator systems.

Compensation & Benefits

Annual salary range: $180,000 to $230,000 USD. Visa sponsorship is available.

Location

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