Senior Research Engineer: Multi-Agent Learning & ML Systems

Google LLC; Mountain View (CA); On-site; GBP 90,000 - 150,000; Full time

Job summary

Google LLC's DeepMind-inspired research engineers are embedded across the organization to run large-scale experiments, prototype architectures, and publish findings with universities and research institutes worldwide.

The role emphasizes machine learning, multi-agent learning, and building scalable research pipelines on TPU clusters, while mentoring junior engineers and collaborating with researchers to translate theory into production-ready code.

Qualifications

• Experience in multidisciplinary research, guiding, and mentoring.
• Machine Learning experience.
• PhD in Computer Science with a strong publication history.

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.

Skills

Mentoring

Multidisciplinary research

Machine Learning

Education

PhD in Computer Science with publications

Tools

JAX

Job description

Google LLC's DeepMind-inspired research engineers are embedded across the organization to run large-scale experiments, prototype architectures, and publish findings with universities and research institutes worldwide.

The role emphasizes machine learning, multi-agent learning, and building scalable research pipelines on TPU clusters, while mentoring junior engineers and collaborating with researchers to translate theory into production-ready code.