Research Engineer: Real-Time Diffusion & World Models

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

Job summary

Google DeepMind seeks a Research Engineer with a proven track record in building and shipping generative models for real-world use. You will work on large-scale diffusion models for real-time video generation, collaborating with modeling, data and eval teams to scale capabilities and interface with product teams to refine deployment scopes.

You will contribute to cross-functional efforts, bridging research ideas with production-ready code, maintaining scalable architectures and driving rapid,

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.

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

Frontier AI research

Education

Bachelor’s degree or equivalent practical experience

PhD in Computer Science, Machine Learning, AI, or related quantitative field

Tools

TPU/GPU custom kernels

JAX

PyTorch

Job description

Google DeepMind seeks a Research Engineer with a proven track record in building and shipping generative models for real-world use. You will work on large-scale diffusion models for real-time video generation, collaborating with modeling, data and eval teams to scale capabilities and interface with product teams to refine deployment scopes.

You will contribute to cross-functional efforts, bridging research ideas with production-ready code, maintaining scalable architectures and driving rapid,