Lead Research Scientist, Post-Training for Consumer Agents

DeepMind Technologies Limited; New York (NY); On-site; USD 262,000 - 364,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid time off

Sick time

Maternity leave

Job summary

Google DeepMind in the United States is seeking a Research Engineer/Research Scientist to advance agent post-training, RL, and personalisation. You will build and scale methods that convert foundation models into autonomous, adaptive consumer agents, focusing on reinforcement learning, targeted data creation, and persistent context for user-specific state.

You will also create training datasets and evaluation frameworks to test long-horizon reasoning and ensure reliable, scalable AI systems

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of software development experience.
• 5 years of design and architecture; testing/launching software products.
• Experience with large language models, reinforcement learning, and deep learning.

Responsibilities

• Specialize in core modeling and post-training experience. Drive the research process for large-scale agent post-training from hypothesis formulation to delivery in the Gemini model recipe.
• Design and execute ablation studies to validate research hypotheses and accelerate experimental feedback loops.
• Communicate research findings, progress, and outcomes to the broader team through visualizations and reports.
• Develop research infrastructure and utilities for data analysis and model evaluations using standard engineering practices.
• Collaborate with other research scientists and engineers to maintain a regular feedback and communication loop.

Skills

Large language models

Reinforcement learning

Deep learning

System design

Technical leadership

Education

Bachelor's degree

Master's degree or PhD in Engineering/CS

Job description

Google DeepMind in the United States is seeking a Research Engineer/Research Scientist to advance agent post-training, RL, and personalisation. You will build and scale methods that convert foundation models into autonomous, adaptive consumer agents, focusing on reinforcement learning, targeted data creation, and persistent context for user-specific state.

You will also create training datasets and evaluation frameworks to test long-horizon reasoning and ensure reliable, scalable AI systems