Research Scientist, Autonomous Agents

Within the team, Research Scientists are encouraged to lead and support a research agenda aimed at producing practically applicable technological advances in the ability of increasingly autonomous agents to assist, support, and empower humans.

The expectation is that Research Scientists will conduct novel research according to ambitious long-term agendas, while maintaining a strong focus on methods and tools offering practical benefits in the short term as a form of pragmatic grounding. Central to this process is the idea that rapid iteration and refinement of solutions catering to real-world use cases provide a strong basis for better understanding the research boundary in a fast-paced field.

Key responsibilities:

• Participate in the ideation and development of new use cases and desired capabilities for human-oriented agents of any form, advised by the current state of research.

• Partner with research engineers to develop ambitious prototypes pertaining to desired or anticipated agent use cases, and design and implement evaluation protocols around these prototypes.

• Identify roadblocks and research challenges—motivated by the empirical study of existing methods' failures or limitations in use-case evaluations—and develop novel technical or methodological solutions to overcome them.

• Identify sources of data, design and implement data collection processes (supported by research engineering partners), and conduct human annotation and evaluation campaigns for the production and evaluation of strong baselines for each use case.

• Help identify, within Google DeepMind’s broad portfolio of research projects, methods that could be adapted or tested against our evaluations. This includes identifying teams and individuals we could partner with to overcome challenges, while providing grounding and evaluation for that collaborator's research agenda.

• A PhD in a technical field or equivalent practical experience. This specific role is targeting recent graduates, and the ideal candidate will be willing to work closely with one or more senior researchers on established, high-value projects.

• Hands-on experience with experiment design and analysis, including data collection and validation methodology, statistical analysis of results and their significance, and performing rigorous ablation studies.

• Experience in a research domain connected to the production of increasingly autonomous human-oriented agents (e.g., LLM-powered agents, RL/IL, applications in NLP, evaluation design).

• A desire to produce the next generation of agentic systems capable of learning from and efficiently adapting to deployment in real-world scenarios.

• Strong end-to-end system-building and prototyping skills.

• Experience with one or more of the following: fine-tuning LLMs, running human data collection/annotation campaigns, self-play, or multi-agent systems.

• Experience with open-ended learning, RL, and frontier methods for training LLMs (RLHF, RLAIF, multi-turn RL, multi-agent interactions, reward function design and modelling, etc.).

• A curiosity about or experience with research topics surrounding personalization, memory, reasoning, self-improvement, and safety.