Senior Research Engineer- Optimization
We are looking for engineers with significant problem solving experience in PyTorch, CUDA and distributed systems. You will work with Research Scientists to build & train cutting edge foundation models on thousands of GPUs.
Responsibilities
Ensure efficient implementation of models & systems for data processing, training, inference and deploymentIdentify and implement optimization techniques for massively parallel and distributed systemsIdentify and remedy efficiency bottlenecks (memory, speed, utilization) by profiling and implementing high-performance CUDA, C++ and PyTorch codeWork closely together with the research team to ensure systems are planned to be as efficient as possible from start to finishBuild tools to visualize, evaluate and filter datasetsImplement cutting-edge product prototypes based on multimodal generative AI Experience
Experience training large models using Python & Pytorch, including practical experience working with the entire development pipeline from data processing, preparation & data loading to training and inference.Experience with profiling CPU & GPU code in PyTorch, including Nvidia Nsight or similar.Experience writing & improving highly parallel & distributed PyTorch code, with familiarity in DDP, FSDP, Tensor Parallel, etc.Experience writing high-performance parallel C++. Bonus if done within an ML context with PyTorch, like for data loading, data processing, inference code.Experience with high-performance CUDA and writing custom PyTorch kernels. Top candidates will be able to utilize tensor cores; optimize performance with CUDA memory and other similar skills.Good to have experience working with Deep learning concepts such as Transformers & Multimodal Generative models such as Diffusion Models and GANs.Good to have experience building inference / demo prototype code (incl. Gradio, Docker etc.)Please note this role is not meant for recent grads. In addition to cash base pay, you'll also receive a sizable grant of Luma's equity.
The pay range for this position is for Bay Area. Base pay offered may vary depending on job-related knowledge, skills, candidate location, and experience.
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