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 systems team building high-performance training and inference infrastructure for image, video, and world-model workloads. You will improve efficiency across GPU kernels, distributed execution, and multi-node training and serving.
WHAT YOU'LL DO
- Optimize system and GPU performance for training and inference workloads.
- Profile and remove bottlenecks at the kernel, memory, system, and cluster levels using Nsight and related tools.
- Implement low-level optimizations with CUDA and Triton.
- Design and improve distributed training and inference engines for diffusion models.
- Improve communication performance across GPUs and nodes using NCCL, RDMA, InfiniBand, RoCE, and disaggregated serving.
- Build benchmarking and regression harnesses to sustain performance gains in production.
- Develop 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, distributed systems, or related ML systems work.
- At least 1 year of hands-on ML systems or GPU performance experience in a full-time role.
- Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, or Megatron.
- Direct NVIDIA GPU kernel development experience using CUDA, CUTLASS, Triton, or PTX, including writing kernels.
- Experience optimizing inference or training performance across GPU kernels, runtimes, or distributed execution.
- Strong machine learning and computer science fundamentals, with a degree in computer science or a related quantitative field.
- Experience with PyTorch, Nsight, NCCL, and RDMA is relevant; experience 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.