Research Engineer, World Models, DeepMind

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.

We are seeking a Research Engineer with a proven track record of building and shipping generative models in the real world. In this role, you will have strong engineering background in building, testing and deploying diffusion models applied to real-time video generation. You will work with our modeling, data and eval teams to integrate and scale up model capabilities. You will also have the opportunity to interface with the product team to refine the scope of our deployments.

You will drive cross-functional collaboration to establish rapid, iterative feedback loops spanning playtesting, real-time user insights, and rigorous evaluation frameworks. This high impact role requires deep direct technical leadership – architecting and maintaining robust, scalable codebases while pioneering advanced AI-driven code generation and automated code review workflows.

Artificial intelligence will be one of 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.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

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.