As a Research Engineer/Research Scientist, you will work on Agent Post-training and Personalization to build Consumer Agents. You will research, develop and scale foundational post-training methods that convert foundation models into autonomous, adaptive agents for consumers. Your focus centers on reinforcement learning, targeted data creation, and persistent context architectures so models retain user-specific state reliably over time and adapt to individual users. Additionally, you will build training datasets and evaluation frameworks to test long-horizon reasoning, uncover failure modes, and measure agent reliability across complex workflows.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google.
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.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience with design and architecture; and testing/launching software products.
- Experience with large language model, reinforcement learning, and deep learning.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 5 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.