Research Engineer, Advancing Agent Quality, DeepMind

• Design, build, and scale realistic agent environments and task suites. Research and develop Agentic AutoRaters (AR), calibrate them against human evaluation, and benchmark agent capabilities against industry-leading frontier models.
• Enable agents to automatically generate and iterate on verifiers (such as automated test cases), facilitating effective exploration and iterative problem-solving.
• Develop trajectory analysis frameworks and diagnostic tooling to identify root-cause agent failure modes (e.g., passivity, hallucination, or brittle tool execution), and automatically optimize agent harnesses to drive continuous self-improvement.
• Harvest complex multi-turn interaction trajectories into high-quality datasets and reward signals to power SFT and RL flywheels for frontier Gemini models.

Minimum qualifications:

• 2 years of experience with agentic AI workflows, frameworks, and approaches.
• 1 year of experience with machine learning and deep learning.
• Experienece in software engineering and cloud-based development.
• Experience with Python, TensorFlow, PyTorch, or similar ML frameworks.

Preferred qualifications:

• In-depth knowledge of machine learning algorithms, including supervised learning and reinforcement learning.

• 2 years of experience with agentic AI workflows, frameworks, and approaches.
• 1 year of experience with machine learning and deep learning.
• Experienece in software engineering and cloud-based development.
• Experience with Python, TensorFlow, PyTorch, or similar ML frameworks.