Requirements:

• 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, Agentic Tool-Use, or Inference-Time Search.

Nice to haves:

• 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 maintaining clean, robust, and production-grade shared codebases.

What you'll be doing:

• Operate across the full research-and-engineering lifecycle of frontier reasoning and agentic systems.
• Bridge research to production by solving unsolved problems in agentic reasoning.
• Architect and optimize distributed post-training pipelines and agent-environment simulation loops.
• Design experiments and failure analyses to isolate performance bottlenecks.
• Maintain high code quality and architectural health across shared Reinforcement Learning and modeling codebases.

Perks and benefits:

US: $207,000 - $300,000 (USD) + 20% bonus target + equity + benefits

Opportunity to share preferred working locations - London, UK; Mountain View, CA, USA; New York, NY, USA

Learn more about benefits at Google.