Member of Technical Staff, ML Systems

About the Role

Join an ML systems team building and improving the training and inference stack for image, video, and world-model workloads. You will focus on making these systems faster and more efficient, from GPU kernels through distributed execution and serving.

What You'll Do

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

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

• Build benchmarks and regression tests to help performance improvements persist in production.

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

• Hands-on experience writing GPU kernels or low-level optimizations with CUDA, CUTLASS, Triton, or PTX on NVIDIA GPUs.

• Experience contributing core features to an inference or training framework, rather than focusing only on deployment or integration.

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

• 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.

Compensation & Benefits

Salary range: $180,000 to $230,000 annually. Visa sponsorship is available.

Location

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