Develops performance optimizations for ML models across graph, kernel, and system levels using PyTorch and Thunder compiler. Builds tools, collaborates with partners, and contributes to open-source while requiring strong PyTorch expertise and optimization experience.
Develop performance-oriented model optimizations at multiple levels:
Advance the Thunder compiler by building optimization passes, graph transformations, and integration hooks to accelerate training and inference workloads. Work across the software stack to ensure optimizations are accessible to end users through clean APIs, automated tooling, and seamless integration with PyTorch Lightning. Design and implement profiling and debugging tools to analyze model execution, identify bottlenecks, and guide optimization strategies. Collaborate with hardware vendors and ecosystem partners to ensure Thunder runs efficiently across diverse backends (NVIDIA, AMD, TPU, specialized accelerators). Contribute to open-source projects by developing new features, improving documentation, and supporting community adoption. Engage with researchers and engineers in the community, providing guidance on performance tuning and advocating for Thunder as the go-to optimization layer in ML workflows. Work cross-functionally with Lightning's product and engineering teams to ensure compiler and optimization improvements align with the broader product vision.
As a Software Engineer on the Inference Stack team, you will build the distributed runtime that powers large-scale LLM inference. This role involves working across the stack, from developer experience to low-level infrastructure, and owning systems in production.
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