Offer summary

Qualifications:

Significant problem-solving experience in PyTorch, CUDA, and distributed systems., Experience training large models using Python & PyTorch, including the entire development pipeline., Familiarity with high-performance parallel C++ and optimization techniques for CUDA and Triton., Good understanding of deep learning concepts such as Transformers and Multimodal Generative models..

Key responsibilities:

Ensure efficient implementation of models and systems for data processing, training, inference, and deployment.

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

Work closely with the research team to ensure systems are planned for maximum efficiency.

Build tools to visualize, evaluate, and filter datasets.

Job description

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 high-performance 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 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 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 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 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.)

Compensation

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

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