Responsibilities
- Contribute to the creation of novel multi-agent learning algorithms and frameworks, with a focus on performance and scalability in Just-In-Time (JIT) compilation, Autograd, and XLA (JAX).
- Build and maintain large-scale simulation platforms and end-to-end research pipelines to run experiments on Google DeepMind’s cutting-edge infrastructure, including massive TPU pods.
- Partner deeply 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, establish best practices for code quality and maintainability, and mentor 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.
Basic qualifications
- Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience.
- 5 years of experience in software development using Python or C++.
- Experience in deep learning frameworks, such as JAX or PyTorch.
- Experience in standard analysis and scientific computing libraries such as numpy, pandas, and matplotlib.
Preferred qualifications
- PhD with a research focus on Machine Learning, Reinforcement Learning, or Multi-Agent Systems.
- Experience training large-scale models on accelerators (TPUs, GPUs) in a distributed environment.
- Experience working with language models such as designing agentic harnesses, memory retrieval, or fine-tuning.
- A track record of leading complex software projects and a passion for enabling groundbreaking research through engineering.
- Deep expertise building and optimizing complex systems in JAX.
- A strong background in multi-agent reinforcement learning, algorithmic game theory, or computational economics.
About the company
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 learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.