Member of Technical Staff, ML Systems
Description
About the Role
Join an AI and machine learning team building high-performance training and inference systems for image, video, and world-model workloads. You will improve performance across the stack, from GPU kernels to distributed training and serving.
What You'll Do
Optimize training and inference performance for 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 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 benchmarks and regression tests to preserve performance gains in production.
Co-design kernels, runtimes, and models with hardware in mind.
What We're Looking For
At least 2 years of experience in deep learning training or inference systems, GPU performance, or distributed systems.
Hands-on ML systems or GPU performance experience in a recent role, including work on inference or training performance.
Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, or Megatron.
Direct experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX.
Strong machine learning and computer science 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 is $180,000 to $260,000 annually. Visa sponsorship is available.
Location
On-site in Menlo Park, California, United States.
Skills
As published by ashby · 2 questions
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