Member of Technical Staff, ML Systems

About the Role

Join an AI systems team building and optimizing the training and inference stack for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed engines, and multi-node 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 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 performance across GPUs and nodes using technologies such as NCCL, RDMA, InfiniBand, and RoCE.

• Build benchmarking and regression tools to ensure performance improvements hold in production.

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

What We're Looking For

• 2 to 10+ years of relevant experience, including at least one year in deep-learning training or inference systems, or distributed systems.

• Hands-on experience developing low-level GPU optimizations with CUDA or Triton.

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

• Familiarity with PyTorch, Nsight, and performance optimization for machine learning workloads.

Compensation & Benefits

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

Location

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