Research Engineer, World Models, DeepMind

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.

In this role, you will implement core infrastructure and conduct research to build generative models of the physical world. You will solve problems to train world simulators, develop metrics for physical intelligence, curate and annotate training data, and enable real-time interactive generation. Additionally, you will develop methods for systems and infrastructure.

Artificial intelligence will be one 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.

Responsibilities

• Build infrastructure for video data pipelines and annotation
• Optimize inference and distillation for real-time generation.
• Conduct research for video pretraining.
• Develop next generation forms of interactivity.
• Develop methods for long-term memory in world models.

Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of software development experience using Python or C++.
• Experience with deep learning frameworks (e.g., JAX or PyTorch).
• Experience with analysis and scientific computing libraries (e.g., NumPy, Pandas, and Matplotlib).

Preferred qualifications:

• PhD with a research focus on Machine Learning, Reinforcement Learning, or Multi-Agent Systems.
• Experience training models on accelerators (TPUs, GPUs) in a distributed environment.
• Expertise building and optimizing complex systems in JAX.