World Models Research Engineer (Real-Time Diffusion)

Google Inc.; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Google DeepMind in London is seeking a Research Engineer to advance world models and deploy diffusion-based video generation at scale. You will work across modeling, data and eval teams to prototype architectures and push the boundaries of AI research into practical applications.

This role emphasizes engineering leadership, building robust, scalable codebases, and collaborating with product teams to refine deployment scopes while contributing to a global research community.

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 for model serving.
• PhD in CS/ML/AI or related field (preferred).
• Experience deploying ML models to production at scale or publishing open-source research.

Responsibilities

• 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.

Skills

Diffusion models

Generative video architectures

GPU/TPU kernels

JAX

PyTorch

Education

Bachelor’s degree or equivalent practical experience

PhD in CS/ML/AI or related field

Tools

JAX

PyTorch

Job description

Google DeepMind in London is seeking a Research Engineer to advance world models and deploy diffusion-based video generation at scale. You will work across modeling, data and eval teams to prototype architectures and push the boundaries of AI research into practical applications.

This role emphasizes engineering leadership, building robust, scalable codebases, and collaborating with product teams to refine deployment scopes while contributing to a global research community.