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About the Role
At DeepMind, the Planet-Scale Resources, Infrastructure and Systems Management (PRISM) team advances AI reasoning and autonomous agentic systems by integrating research and execution. This role involves operating across the full research-and-engineering lifecycle, developing distributed post-training infrastructure and algorithms to enable Gemini models to solve complex, multi-step problems autonomously. Google DeepMind is a pioneering AI lab focused on advancing AI development for global challenges and product innovation, prioritizing safety and ethics.
Responsibilities
- Operate across the full research-and-engineering lifecycle of frontier reasoning and agentic systems.
- Work on unsolved problems in agentic reasoning, turning early exploratory prototypes into hardened production features for Gemini releases.
- Architect and optimize distributed post-training pipelines and agent-environment simulation loops across thousands of accelerators.
- Design rigorous experiments and failure analyses to isolate performance bottlenecks and communicate findings through clear write-ups.
- Maintain high code quality and architectural health across shared reinforcement learning and modeling codebases.
Requirements
- Bachelor's degree in Computer Science, Mathematics, Physics, a related quantitative field, or equivalent practical experience.
- 4 years of experience building, scaling, and debugging machine learning models using deep learning frameworks (e.g., JAX, PyTorch, or TensorFlow).
- Experience in one core area: Reinforcement Learning (RL), Post-Training (SFT/RLHF/RLAIF), Agentic Tool-Use, or Inference-Time Search.
- PhD in Computer Science, Machine Learning, Physics, or a related quantitative field.
- Experience training and managing models on large-scale distributed accelerator clusters (e.g., TPUs or GPUs).
- Experience designing asynchronous agent-environment simulation loops or large distributed post-training pipelines.
- Experience prototyping new hypotheses quickly while keeping shared codebases clean, robust, and production-grade.
Skills
- Deep learning frameworks (JAX, PyTorch, TensorFlow)
- Reinforcement Learning (RL)
- Post-Training (SFT/RLHF/RLAIF)
- Agentic Tool-Use
- Inference-Time Search
- Large-scale distributed accelerator clusters (TPUs, GPUs)
- Asynchronous agent-environment simulation loops
- Large distributed post-training pipelines
- Prototyping
- Code quality
- Architectural health
Location
- US
Work Type
- Full-time
Experience Level
- 4 years of experience
- PhD
Education Level
- Bachelor's degree
- PhD
Salary/Compensations
- USD $207000 - $300000
- 20% bonus target
- Equity
Benefits
- Benefits at Google
About the Company
- At DeepMind, the Planet-Scale Resources, Infrastructure and Systems Management (PRISM) team brings together researchers and engineers to advance the frontiers of AI reasoning and autonomous agentic systems.
- Our work powers Gemini & Gemma—developing core reasoning capabilities and RL scaling for Gemini 3, and leading Gemma 3 270M, including multi-agent Gemini capabilities.
- We deliver critical contributions to AI Grand Challenges (such as our gold medal-winning IMO 2025 effort), drive product innovations like 'deep think' mode and agentic inference scaling in antigravity, and lead Alphabet-wide initiatives including AI for Science and Project Big Sleep.
- 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.
Equal Opportunity
- Individual pay is determined by factors including job-related skills, experience, and relevant education or training.