Senior Research Engineer, Agentic Data and Tooling, DeepMind

• Build agentic data infrastructure at high velocity: Own and deliver key components across the agentic data stack. Rapidly prototype, iterate, and ship robust production code to generate, capture, and curate complex multi-turn agent trajectories at scale.
• Bridge research and engineering to drive model hillclimbing: Collaborate closely with Gemini research teams to close the loop between data creation and model quality. Understand how multi-turn trajectory design, environment complexity, and reward signals impact SFT, RL training, and capability hillclimbing. Directly integrate curated data into training pipelines and evaluate downstream model performance on frontier benchmarks.
• Build quality tooling and support gold-standard benchmarks: Create human-in-the-loop annotation tooling and interactive trajectory review surfaces, working in tandem with automated validation checkers leveraging adversarial LLM judges and programmatic verifiers. Support the creation and curation of gold-standard evaluation sets for flagship benchmarks.

Minimum qualifications:

• Bachelor's degree in Computer Science, Information Technology, a related technical field, or equivalent practical experience.
• 5 years of experience working with large language models (LLMs).
• 2 years of experience developing and training machine learning models.
• Experience with Agentic integrations and Model Context Protocol.

Preferred qualifications:

• Master's degree or PhD in Electrical Engineering, Computer Science, or equivalent practical experience.
• 2 years of experience with full-stack development.
• Excellent analytical, problem-solving and communication skills with demonstrated attention to detail.
• A deep passion for AI technology and all of its possibilities .

• Bachelor's degree in Computer Science, Information Technology, a related technical field, or equivalent practical experience.
• 5 years of experience working with large language models (LLMs).
• 2 years of experience developing and training machine learning models.
• Experience with Agentic integrations and Model Context Protocol.