Member of Technical Staff, ML Systems

About the Role

Join an AI/ML team building and improving training and inference systems for image, video, and world-model workloads. You will drive performance across GPU kernels, distributed systems, and serving infrastructure.

What You'll Do

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

• Profile bottlenecks across GPU kernels, memory, systems, and clusters using Nsight and related tools.

• Implement low-level optimizations using 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 help maintain performance gains in production.

• Apply hardware-aware co-design across kernels, runtimes, and models.

What We're Looking For

• At least 2 years of experience in deep learning training or inference systems, or distributed systems, with 1 or more years of hands-on ML systems or GPU performance work in a recent role.

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

• Hands-on experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX.

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

• 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 relevant.

Compensation & Benefits

Base salary: $180,000 to $230,000 annually. Visa sponsorship is available.

Location

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