Machine Learning Compiler Engineering Manager, GPU

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development in C or C++.
• 3 years of experience in a technical leadership role; overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.
• Experience in compiler construction or related fields.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience working on or leading a team to work on a compiler stack, (e.g., Low Level Virtual Machines (LLVM)).
• Experience with Machine Learning (ML) frameworks, such as JAX/PyTorch/MLIR/TensorFlow etc.
• Experience working on HPC/GPU workloads.

Experience working on or leading a team to work on a compiler stack, (e.g., Low Level Virtual Machines (LLVM)).

About the job

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

The Core ML Foundations Compiler team builds Accelerated Linear Algebra (XLA) the compiler technology that powers machine learning frameworks at Google, which includes JAX, PyTorch/XLA and TensorFlow.

The compiler makes competitive performance possible on cutting-edge ML workloads like Gemini, LLaMa, Veo and next-generation Large-Language Models (LLMs).
In this role, you will contribute to and use JAX, PyTorch/XLA and TensorFlow infrastructure and own the CPU and Graphics Processing Unit (GPU) production ML compilers.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

The compiler makes competitive performance possible on cutting-edge ML workloads like Gemini, LLaMa, Veo and next-generation Large-Language Models (LLMs).
In this role, you will contribute to and use JAX, PyTorch/XLA and TensorFlow infrastructure and own the CPU and Graphics Processing Unit (GPU) production ML compilers.

Responsibilities

• Lead and manage a team of Software Engineers to accelerate ML applications, like Large Language Models and Generative AI, to run at the fullest potential on GPUs. 
• Enhance the GPU compiler software stack so that it performs faster and more efficiently on cutting-edge ML workloads.
• Launch new GPU optimizations, including performance and correctness testing.
• Foster deep collaboration between customers, partners (e.g., GPU manufacturers, researchers and others) and the GPU compiler team.
• Support and develop members of the team. Meet regularly with individuals to discuss performance and development and provide feedback and coaching while also growing the overall compiler team in Munich.