Research Engineer, World Models, DeepMind

• Architect and implement next-generation model architectures in collaboration with research teams to advance frontier world models.
• Design and iterate on real-world evaluation pipelines to systematically measure and accelerate model capabilities.
• Engineer high-performance model distillation and optimization techniques to support real-time serving at global scale.
• Build scalable data ingestion pipelines and automated filtering mechanisms, using rigorous dataset analysis to maximize training quality and drive downstream benchmark gains.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience developing, training, and deploying diffusion models and generative video architectures.
• Experience writing custom GPU/TPU kernels (e.g., JAX or PyTorch) for model serving.

Preferred qualifications:

• PhD in Computer Science, Machine Learning, AI, or a related quantitative field or equivalent practical experience.
• Experience in frontier AI research labs within pre-training or post-training teams.
• Proficiency in writing TPU/GPU custom kernels to optimize model performance and inference.
• Proven track record of deploying machine learning models to production at scale or publishing widely adopted open-source research.

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience developing, training, and deploying diffusion models and generative video architectures.
• Experience writing custom GPU/TPU kernels (e.g., JAX or PyTorch) for model serving.