- Architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization.
- Work closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges.
- Work on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations.
- Develop compiler optimizations and passes that convert high-level AI models (e.g., from TensorFlow, PyTorch) into intermediate representations (IR).
- Implement parsing, semantic analysis, and IR generation for deep learning frameworks.
- Research and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers.
- Provide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations.
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