Software Engineer for Large Models

You'll be building machine learning infrastructure used to make these models scalable, performant, robust, and reusable—enabling and contributing to future research breakthroughs by applying trained models in new and novel ways.

Engineering challenges we solve help build scalable and reusable technology for model use across multiple research projects. Alongside researchers we are also interested in approaches that make large models more performant and less resource intensive (recent examples include quantization, various sharding regimes, etc.)

You'll work alongside world-class research projects that are constantly pushing the boundaries in the fields of large models, heterogeneous compute, distributed computation on accelerators, and large model RL training—to name a few.

Your colleagues will be Software and Research Engineers with a diverse set of backgrounds working to accelerate DeepMind's mission and research goals. Our team's solid fundamentals across both engineering and research makes us well suited to make the use of large models maximally permissive internally.

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• Excellent knowledge of either C++ or Python
• Candidates should have an in-depth knowledge in at least one of the following areas, and will gain familiarity with all of the below through on-the-job learning.

-Using HW accelerators (GPU / TPU)

-Large-scale system design

-Low-level data management

• Compiler (e.g. MLIR) familiarity
• Transformer architecture

In addition, the following would be an advantage:

• Experience implementing, evaluating, and fine-tuning ML algorithms
• Software engineering in multimodal settings

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