Member of Technical Staff, ML Systems

About the Role

Join an AI systems and infrastructure startup focused on improving the performance of machine learning training and inference. You will work across GPU kernels, distributed execution, and serving systems for image, video, and world-model workloads, helping make these systems 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, and RoCE.

• Build benchmarking and regression tools to track performance improvements 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 ML systems performance.

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

• Experience contributing core features to a training or inference 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 is relevant.

Compensation & Benefits

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

Location

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