> Markdown version of [/jobs/ext/4429062-software-engineer-ml-accelerators-deepmind](https://www.wearedevelopers.com/jobs/ext/4429062-software-engineer-ml-accelerators-deepmind). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, ML Accelerators, DeepMind - **Company:** DEEPMIND MATRIX - **Location:** Mountain View, CA, United States - **Experience:** Experienced - **Salary:** $174,000.0 - $252,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computing Platforms, Machine Learning, Systems Development Life Cycle, Software Engineering, Hardware Acceleration, Model Inference, DeepMind - **Published:** October 13, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=dc144e3b3142ed8e ## About the Role * 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., * 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. About the job 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. ## Description * 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.