Research Scientist, World Modeling

As a Research Scientist in World Modeling at Google DeepMind, your key responsibilities will include implementing core infrastructure, conducting research to create generative models that simulate the physical world, and solving critical problems necessary for training world simulators at scale. You'll also focus on developing metrics, curating training data, and enabling real-time interactive generation.

To qualify for the Research Scientist position in World Modeling at Google DeepMind, candidates should possess an MSc or PhD in computer science or machine learning, or have equivalent industry experience. A strong background in large-scale transformer models and data pipelines, along with a proven track record of publications or releases related to video generation or multimodal language models, is essential.

In the Research Scientist role at Google DeepMind, you'll be engaging with technologies such as JAX or PyTorch for deep learning, as well as working extensively on large-scale video data pipelines and systems for training multimodal transformers. Familiarity with these frameworks is crucial for success in this position.

At Google DeepMind, you can expect a collaborative and inclusive team culture that values diversity of thought and experiences. You'll be working with scientists, engineers, and machine learning experts who share a passion for advancing AI for the benefit of society. We prioritize safety and ethics in all of our projects while encouraging innovative ideas.

The US base salary range for the Research Scientist role in World Modeling at Google DeepMind is between $136,000 - $245,000 per year, in addition to bonuses, equity, and benefits. Specific salary details will be discussed during the hiring process.

When answering this question, provide specific examples of projects where you've successfully utilized large-scale transformer models. Highlight the frameworks you used, challenges you faced, and how your contributions helped in model performance or infrastructure.

Discuss specific optimization techniques you've implemented, such as gradient accumulation or mixed precision training. Explain how these methods improved compute efficiency and reduced training time for your models.

Use the STAR method to structure your response, detailing the Situation, Task, Action, and Result. Focus on a specific challenge related to generative modeling and illustrate how you approached it and what the outcomes were.

Explain the strategies you employ for data curation, such as strict validation protocols or using automated tools for error detection. Discuss how maintaining high data quality impacts training performance.

Share insights on current trends in world modeling, such as advancements in multimodal learning or improvements in inference techniques. Discuss why these trends are important and how they impact the future of AI.

Discuss the quantitative and qualitative metrics you utilize to assess model performance. Include specific examples like FID (Fréchet Inception Distance) or human evaluations and explain how these evaluations inform your model improvements.

Highlight your communication skills and adaptability. Explain how you ensure effective collaboration by integrating feedback from team members across different expertise areas and fostering a supportive work environment.

Provide an example where you initially pursued a complex approach but eventually shifted to a simpler method that yielded better results. Discuss what you learned from this experience and how it applies to the principle of simplicity in research.

Discuss the resources you use, such as academic journals, conferences, workshops, or online courses. Explain how you apply the knowledge gained to your work, demonstrating your commitment to continuous learning.

Share personal motivations for wanting to work at Google DeepMind, emphasizing the opportunity to contribute to significant advancements in AI. Discuss how the company’s values align with your passion for impactful, ethical AI research.