Member of Technical Staff, ML Systems
True Talent Solutions Partners · Menlo Park · onsite
ABOUT THE ROLE
Join an AI and machine learning team building efficient training and inference systems for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and multi-node serving, helping make demanding model workloads faster and more efficient.
WHAT YOU'LL DO
- Optimize training and inference performance for image, video, diffusion, and world-model workloads.
- Profile bottlenecks across kernels, memory, systems, and clusters using Nsight and related tools.
- Implement low-level GPU 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 benchmarks and regression tests to ensure performance improvements hold in production.
- Contribute to 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 work, with hands-on ML systems or GPU performance experience.
- Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, Megatron, or a similar system.
- Hands-on experience writing GPU kernels for NVIDIA hardware using CUDA, CUTLASS, Triton, or PTX.
- Strong machine learning and computer science fundamentals, typically supported by a degree in computer science or a related quantitative field.
- Experience with PyTorch, Nsight, NCCL, and RDMA is relevant.
- Background optimizing inference or training performance, ideally for diffusion, video, image, or other multimodal workloads.
COMPENSATION & BENEFITS
Base salary: $180,000 to $260,000 annually. Visa sponsorship is available.
LOCATION
On-site in Menlo Park, California, United States.