Member of Technical Staff, ML Systems
True Talent Solutions Partners · Menlo Park · onsite
USD 180,000 to 260,000 per year
ABOUT THE ROLE
Join an AI/ML 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 model workloads faster and more efficient.
WHAT YOU'LL DO
- Optimize system and GPU performance for training and inference across image, video, and world-model workloads.
- Profile bottlenecks at the kernel, memory, system, and cluster levels 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 ensure performance improvements persist in production.
- Co-design kernels, runtimes, and models with target hardware 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, with 1 or more years of hands-on GPU performance experience in a recent role.
- Experience authoring core features in an inference or training framework, rather than focusing only on deployment or integration.
- Direct experience writing GPU kernels for NVIDIA hardware using CUDA, CUTLASS, Triton, or PTX.
- Strong computer science and machine learning fundamentals, with a degree in computer science or a related quantitative field.
- Experience with PyTorch, Nsight, NCCL, and RDMA, and performance optimization for inference or training.
- Background optimizing diffusion, video, image, or other multimodal workloads is valuable.
COMPENSATION & BENEFITS
Base salary is $180,000 to $260,000 annually. Visa sponsorship is available.
LOCATION
On-site in Menlo Park, California, United States.