Staff ML Compiler Engineer, TPU Performance Optimizations

• Learn and build an intuitive understanding of various parts of Google’s ML stack: Frameworks (e.g., JAX, PyTorch), XLA and runtime stack.
• Research and develop the novel compiler optimizations targeting ML workloads, and emerging architectures.
• Identify opportunities to improve the efficiency of the ML workloads through insightful performance debugging for ML workloads and custom kernels, and build compiler solutions to deliver those improvements.
• Provide technical leadership and mentorship as a Team Lead (TL), and explore strategic initiatives.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 3 years of experience with software design and architecture.
• Experience with machine learning, compilers, computer architecture, GPU programming, C++, Python.

Preferred qualifications:

• Experience in open-source software development, including experience in releasing and supporting open-source projects.
• Experience with ML compilers and their internals, experience writing compiler optimization passes.
• Experience with debugging correctness and performance issues at all levels of the ML Software (SW) stack.
• Experience with GPU or TPU performance analysis.
• Comfortable with accelerator Hardware (HW) architectures (TPUs/Graphics Processing Unit (GPUs)).
• Comfortable debugging correctness and performance issues at all levels of the stack.

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 3 years of experience with software design and architecture.
• Experience with machine learning, compilers, computer architecture, GPU programming, C++, Python.