Solution Architect — Core ML Frameworks, Kernels & Performance

In this role, you will drive the performance and optimization of both training and serving, delivering massive impact for customers. We are seeking a talented Kernel Engineer to join the highly interdisciplinary CoreML team. You will have exposure to the newest Tensor Processing Unit (TPU), Graphics Processing Unit (GPU) hardware, the latest ML models, and the advanced toolchains that bridge them. Your work will directly enable AI research and production deployments across Google Cloud, and the broader open-source ecosystem. You will address complex technical issues that directly impact the efficiency and scalability of AI across the industry. Responsibilities Design and optimize high-performance kernels (using languages like Pallas, Mosaic, and Triton) targeting Tensor Processing Unit (TPU) and Graphics Processing Unit (GPU) architectures for critical Machine Learning (ML) operations, redefining what’s possible from massive training runs to high-speed inference Architect infrastructure such as benchmarking suites, autotuning frameworks, performance analysis tools, regression testing, and documentation, transforming how the developer community interacts with increasingly critical custom kernels in key Open-Source Software (OSS) libraries Track the latest advancements in hardware architectures, compiler technologies, and AI models to identify new opportunities for performance optimization through custom kernels Engage with ML researchers, framework developers (Just After eXecution (JAX), PyTorch), and compiler engineers (Accelerated Linear Algebra (XLA)) to enhance adoption, identify new requirements, and address bottlenecks by providing appropriate solutions Requirements Overall, 12+ years of industry experience 5+ years of experience with software development in C++ or Python 3+ years of experience testing, maintaining, or launching software products, and at least 1 year of experience with software design and architecture Experience with performance optimization at the kernel level Experience optimizing TPU/GPU code using low-level kernel languages like Pallas, Compute Unified Device Architecture (CUDA), or Triton Knowledge of ML frameworks (JAX/PyTorch) and common operations like attention and Mixture of Experts (MoEs), including model optimization and low-precision formats Understanding of modern accelerators (e.g., data movement, pipelining, heterogeneous compute, and scale-out) Understanding of compiler principles (optimization, code generation) and toolchains such as MLIR and OpenXLA Demonstrated record of building developer infrastructure, including Open-Source Software (OSS) libraries, flexible high-performance APIs, and easy-to-consume documentation to empower the community Excellent investigative and problem-solving capabilities, with strong communication skills across cross-functional teams