Research Engineer, Responsible Frontier AI Research, DeepMind

Artificial intelligence will be one of humanitys most transformative inventions. At 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 learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Minimum qualifications:

• Bachelor's degree in Computer Science, Machine Learning, Mathematics, or a related technical field, or equivalent practical experience.
• 3 years of experience in Python programming.
• 3 years of experience with ML frameworks such as JAX, PyTorch, or TensorFlow.

Preferred qualifications:

• Master's degree or PhD in Computer Science, Engineering, or a related field with a focus on machine learning.
• Experience in Python and C++ for high-performance ML library development.
• Experience with harmful manipulation detection, persuasion modeling, deceptive behavior analysis, or AI safety evaluation and mitigation.
• Experience working directly on AI safety, or responsible AI research.
• Experience building evaluation frameworks, benchmarks, or automated testing pipelines for ML models.

Responsibilities:

• Be able to rapidly prototype and deliver scalable engineering solutions across the Responsibility research portfolio.
• Architect and optimize training and inference pipelines to detect and evaluate harmful manipulation behaviors in frontier language models.
• Develop post-training strategies to mitigate manipulation risks including deceptive persuasion, sycophancy, and covert influence tactics.
• Collaborate with research scientists to translate safety research into robust implementations and present results to cross-functional stakeholders.
• Build and maintain evaluation infrastructure to systematically track model safety performance across releases.