Job description

At DeepMind, the Planning, Reasoning, Inference and Structured Models (PRISM) team brings together researchers and engineers to advance the frontiers of AI reasoning and autonomous agentic systems. We reject the false tradeoff between research and execution, pursuing breakthroughs on open AI challenges while embedding directly into core teams to land those capabilities in production. Our work powers Gemini and Gemma by developing core reasoning capabilities and RL scaling for Gemini 3, and leading Gemma 270M, including multiagent 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. In this role, you will operate across the full research and engineering lifecycle, developing distributed post training infrastructure and algorithms that enable Gemini models to solve complex, multistep problems autonomously. 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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google . • Operate across the full research-and-engineering lifecycle of frontier reasoning and agentic systems. • Address 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. • Drive technical excellence by maintaining high code quality and architectural health across shared reinforcement learning and modeling codebases. • Bachelor's or Master'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 at least one core area: Reinforcement Learning (RL), Post-Training (SFT/RLHF/RLAIF), Agentic Tool-Use, or Inference-Time Search. Minimum qualifications: • Bachelor's or Master'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 at least one core area: Reinforcement Learning (RL), Post-Training (SFT/RLHF/RLAIF), Agentic Tool-Use, or Inference-Time Search. Preferred qualifications: • PhD in Computer Science, Machine Learning, Physics, or a related quantitative field. • 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.

Requirements

Must-haves

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.
Learn more about benefits at Google .
Operate across the full research-and-engineering lifecycle of frontier reasoning and agentic systems.
Address 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.
Drive technical excellence by maintaining high code quality and architectural health across shared reinforcement learning and modeling codebases.
Bachelor's or Master'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 at least one core area: Reinforcement Learning (RL), Post-Training (SFT/RLHF/RLAIF), Agentic Tool-Use, or Inference-Time Search.
Bachelor's or Master'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 at least one core area: Reinforcement Learning (RL), Post-Training (SFT/RLHF/RLAIF), Agentic Tool-Use, or Inference-Time Search.

Nice-to-haves

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
PhD in Computer Science, Machine Learning, Physics, or a related quantitative field.
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learning

Platform: google
Effort: medium