Research Scientist/Engineer, Frontier Reasoning, DeepMind

• 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.

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.

• 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.