Software Engineering Manager, AI/ML

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 JAX Models and Performance team empowers GCP customers’ Gen AI workloads by supporting and optimizing performance for key model architectures. We are part of the ML Training team in the AI Engine organization in Core ML. Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google . Provide coaching, mentorship, and career growth for engineers working across large-scale distributed ML systems, performance optimization, and open-source generative AI models. Partner with Tech Leads, product management, and cross-functional teams to define and execute the roadmap for scalable JAX-based LLM (MaxText) and Diffusion (MaxDiffusion) frameworks. Guide the team in implementing core performance optimizations (e.g., quantization, sparsity, custom kernels) and supporting new accelerator architectures (TPUs and GPUs) on GCP AI Hypercomputer. Collaborate closely with DeepMind, Core ML, and the open-source community to deliver high-quality, reusable software artifacts and benchmark-setting MLPerf submissions. Balance rapid model innovation with sustainable engineering practices, operational reliability, and high-trust team environment. Minimum qualifications: Bachelor’s degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 5 years of experience leading ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 3 years of experience in a technical leadership role. 2 years of experience in a people management or team leadership role. 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. Bachelor’s degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 5 years of experience leading ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 3 years of experience in a technical leadership role. 2 years of experience in a people management or team leadership role.