Overview
In this role, you will help advance frontier reasoning and agentic systems, turning exploratory ideas into production features for Gemini releases. You will architect distributed post-training pipelines and simulate agent-environment loops at scale, diagnosing bottlenecks through rigorous experiments. You’ll maintain high-quality, production-grade code across reinforcement learning and modeling systems. This position offers impact through scaling AI reasoning capabilities and contributing to ambitious AI challenges in a fast-paced, collaborative environment.
Pay / Benefits
- bonus target 20%
- equity
- benefits
- opportunities to impact billions of users
- production-grade environment
- career development
Responsibilities
- Develop distributed post-training infrastructure and algorithms for Gemini models
- Design and optimize agent-environment simulations and large-scale pipelines
- Conduct rigorous experiments and failure analyses to isolate performance bottlenecks
- Maintain code quality and architectural health across shared ML and modeling codebases
- Collaborate with cross-functional teams to land capabilities in production for Gemini/Gemma
- Contribute to AI Grand Challenges and product innovations like deep think and scaled inference
Key requirements
- Bachelor in Computer Science, Mathematics, Physics, or related quantitative field
- 4 years of experience building, scaling, and debugging ML models using JAX, PyTorch, or TensorFlow
- Experience in one core area: RL, Post-Training (SFT/RLHF/RLAIF), Agentic Tool-Use, or Inference-Time Search
- strong communication of findings
- problem solving at scale
- collaboration across cross-functional teams
- JAX
- PyTorch
- TensorFlow
Research Scientist/Engineer, Frontier Reasoning, DeepMind in London employer: Google
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