Research Scientist, Gemini Agent Safety, DeepMind

About the job

The Gemini Safety team is accountable for the standards of GDM’s flagship releases. In this role, you will develop algorithmic solutions to advance user-facing architectures. The workstyle is changing, supported by a strong internal culture of mutual dedication and cooperation.

You will bring LLM post-training expertise to ensure outputs are aligned and production-ready. This scope encompasses deep thinking and deep research capabilities, coordinating directly with the Gemini post-training organization and core Product Area counterparts.

You will be a builder and shipper who takes end-to-end ownership. You will formulate mitigation frameworks and evaluations while guiding the cross-functional alignment required to deploy across launch surfaces.

Minimum qualifications:

• PhD in Computer Science, a related field, or equivalent practical experience.
• 1 year experience with Generative AI, Large Language Models, natural language processing, or Agent-based systems.

Preferred qualifications:

• Experience in AI safety and model alignment.
• Experience developing AI agents, safety-critical systems, or Machine Learning Infrastructure.
• Expertise in reward modeling, Reinforcement Learning (RL) for LLMs, instruction tuning, and long-range RL.
• Ability to drive research concepts to product realization with a strong track record of engineering abilities and experimental work.
• Track record of publications at AI/ML venues (e.g., NeurIPS, ICLR, ICML, EMNLP, AAAI, UAI).

Responsibilities

• Design, implement, and maintain high-quality protocols to identify safety gaps in model behavior across all Gemini and GenAI models, and feed these insights back into the post-training process to continuously improve both in-model and out-of-model protection.
• Explore, adapt, and apply innovative modeling and alignment techniques to solve real-world safety and alignment challenges under tight launch timelines.
• Drive innovation in model optimization, advancing the application of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) techniques at massive scale.
• Partner closely with post-training, evaluation, and product teams to align incentives, close safety gaps, and influence safety decisions without direct authority.