Join an ML systems team building and improving the training and inference stack for image, video, and world-model workloads. You will focus on making these systems faster and more efficient, from GPU kernels through distributed execution and serving.
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 with CUDA and Triton.
Design and improve distributed training and inference engines for diffusion models.
Improve communication across GPUs and nodes using technologies such as NCCL, RDMA, InfiniBand, and RoCE.
Build benchmarks and regression tests to help performance improvements persist in production.
Co-design kernels, runtimes, and models with hardware performance in mind.
At least 2 years of experience in deep learning training or inference systems, distributed systems, or related ML systems work.
Hands-on experience writing GPU kernels or low-level optimizations with CUDA, CUTLASS, Triton, or PTX on NVIDIA GPUs.
Experience contributing core features to an inference or training framework, rather than focusing only on deployment or integration.
Experience with multi-GPU or multi-node communication technologies such as NCCL, RDMA, InfiniBand, or RoCE.
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.
Salary range: $180,000 to $230,000 annually. Visa sponsorship is available.
On-site in Menlo Park, California, United States.
Job details are sourced from the employer's original posting.
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