Software Engineer, ML Accelerators, DeepMind

We are building a next-generation compute platform for AI inference workloads, with an emphasis on memory technologies, Hardware (HW) acceleration, and Software (SW)/HW codesign. We are looking for enthusiastic engineers to join the project. This is a coding role for engineers who like to take initiative and get things done, with a focus on ML compiler stack and inference infrastructure. At DeepMind you will be joining our team of scientists and engineers who are directly impacting the future of machine learning for Google and industry. Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . Direct full-stack Software (SW) role, focusing on ML compiler and inference infrastructure. Design and develop ML compiler stack to bridge AI workloads and low-level Hardware (HW) operators. Design and develop SW infrastructure to enable high performance inference. Drive SW/HW codesign, kernel optimization, performance tuning, and debugging. Minimum qualifications: Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience. Experience in software development using Python and C++. Experience with machine learning (ML) hardware accelerators, ML compiler stacks (e.g., JAX, PyTorch, XLA), and kernel development. Preferred qualifications: PhD degree in Computer Engineering, Computer Science, or a related field. 2 years of experience with full-stack system development for hardware accelerators. 2 years of experience with collaborative, hyper-scalar software development and engineering best practices. Experience scoping, planning, and executing projects towards team goals in ambiguous environments.