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 a technical team building training and inference systems for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and multi-node training and serving.
WHAT YOU'LL DO
- Optimize training and inference performance for image, video, and world-model workloads.
- Profile and remove bottlenecks at the kernel, memory, system, and cluster levels using Nsight and related tools.
- Implement low-level optimizations in 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 approaches.
- Build benchmarking and regression harnesses to preserve performance improvements in production.
- Co-design kernels, runtimes, and models with hardware considerations in mind.
WHAT WE'RE LOOKING FOR
- At least 2 years of experience in deep learning training or inference systems, or distributed systems, including at least 1 year of hands-on ML systems or GPU performance work in a recent role.
- Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, Megatron, or a similar system.
- Direct GPU kernel development experience with CUDA, CUTLASS, Triton, or PTX on NVIDIA GPUs.
- Experience with inference or training performance across GPU kernels, runtimes, or distributed execution.
- Strong computer science and machine learning fundamentals, with a degree in computer science or a related quantitative field.
- Experience optimizing diffusion, video, image, or other multimodal workloads is valuable.
COMPENSATION & BENEFITS
Base salary: $180,000 to $260,000 annually. Visa sponsorship is available.
LOCATION
On-site in Menlo Park, California, United States.