Research Engineer Performance 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 deployment

• Identify and implement optimization techniques for massively parallel and distributed systems

• Identify and remedy efficiency bottlenecks (memory, speed, utilization) by profiling and implementing highperformance CUDA, Triton, C++ and PyTorch code

• Work closely together with the research team to ensure systems are planned to be as efficient as possible from start to finish

• Build tools to visualize, evaluate and filter datasets

• Implement cuttingedge 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 optimizing and deploying inference workloads for throughput and latency across the stack (inputs, model inference, outputs, parallel processing etc.)

  • 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 highperformance parallel C++. Bonus if done within an ML context with PyTorch, like for data loading, data processing, inference code.

  • Experience with highperformance Triton 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.)

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  • Compensation

    • The pay range for this position in California is $180,000 $250,000yr; however, base pay offered may vary depending on jobrelated knowledge, skills, candidate location, and experience. We also offer competitive equity packages in the form of stock options and a comprehensive benefits plan.

    • Your applications are reviewed by real people.