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Snapshot

We’re looking for engineers to push forward fundamental research and technology in Artificial Intelligence, as part of our interdisciplinary and collaborative Research Engineering team.

About Us

The Research Engineering team’s home is in Foundational Research at Google DeepMind.

We partner with scientists and engineers from all parts of the organisation, from experimental research, to tackling large scale engineering challenges, to translating research into applications and products.

As a Research Engineer, you’ll use your technical skills and machine learning knowledge in research projects, engineering initiatives, and applications of research.

Your work may involve:

• Working out how to make research methods run with large scale compute.
• Performance engineering, benchmarking, and optimisation.
• Solving key research challenges, via designing and running experiments, sharing analyses and proposing next steps.
• Bringing engineering expertise into research projects. Sharing your skills and knowledge with other engineers and researchers.
• Designing, building, and improving infrastructure for research.

Our projects span the full range of state of the art machine learning and AI fields, including large language models, diffusion models, reinforcement learning, distributed machine learning techniques, and much more.

The Role

We take a holistic view of people’s backgrounds, and do not expect you to be an expert in all areas. We invest a lot of time in training and helping people to continually learn as part of their role.

The core skills we look for in Research Engineers are:

• Degree in computer science, electrical engineering, science, mathematics or equivalent experience.
• Programming experience, particularly with Python-based scientific libraries such as JAX, PyTorch, TensorFlow, NumPy.
• Familiarity with machine learning, mathematics and statistics needed to follow relevant research papers (linear algebra, calculus, etc).

We’re also interested in any of:

• Large-scale system design, distributed systems.
• Distributed computation for ML, especially in the context of accelerators (e.g. sharding, multi-host computation).
• C++ or broader programming experience.
• Data engineering and visualisation.
• Strong communication skills (via discussion, presentation, technical and research writing, whiteboarding).
• Academic research experience in machine learning, publications, or research experience in related fields.