Research Engineer, Multi-Agent Learning, DeepMind

Minimum qualifications:

• Experience in multidisciplinary research, guiding, and mentoring.
• Machine Learning experience.

Preferred qualifications:

• PhD in Computer Science with a strong publication history.

About the job:

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.

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.

Responsibilities:

• Contribute to the creation of novel multi-agent learning algorithms and frameworks, with a focus on performance and scalability in JAX.
• Build and maintain simulation platforms and end-to-end research pipelines to run experiments on DeepMind’s cutting-edge infrastructure, including massive TPU pods.
• Partner with Research Scientists to transform mathematical concepts and research hypotheses into robust, production-quality code and reproducible experiments.
• Lead the engineering direction for complex research projects, establishing best practices for code quality and maintainability, and mentoring junior engineers on the team.
• Optimize every part of the research workflow, from data processing and model training to results analysis, to accelerate the pace of discovery.