Not specified
Member of Technical Staff, ML Systems
- Location
- Menlo Park, United States
- Work type
- On-site · Full-time
- Posted
- (8 hours ago)
About the role
About the Role
Join an AI/ML team building and improving the training and inference stack for image, video, and world-model workloads. You will own performance across GPU kernels, distributed systems, and serving, helping make demanding models faster and more efficient.
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 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 approaches.
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.
At least 1 year of hands-on ML systems or GPU performance experience in your current or most recent role.
Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, Megatron, or a comparable system.
Hands-on experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX. Kernel experience must include direct kernel development.
Strong machine learning and computer science fundamentals, with a degree in computer science or a related quantitative field.
Experience optimizing inference or training performance across GPU kernels, runtimes, or distributed execution.
Experience with 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.