Research Scientist/Engineer, Frontier Reasoning, DeepMind

WeAreTechWomen; Greater London; On-site; GBP 154,000 - 223,000; Full time

Benefits offered by this job

Equity

Bonus target

Benefits

Job summary

Google DeepMind is seeking a researcher/engineer to operate across the full lifecycle of frontier reasoning and agentic systems, developing distributed post-training infrastructure and algorithms that empower Gemini models to solve complex, multi-step problems autonomously.

The role spans unsolved problems in agentic reasoning, turning prototypes into production features for Gemini releases, and architecting scalable post-training pipelines while maintaining shared codebase quality and robust

Qualifications

• Bachelor's degree in a quantitative field or equivalent practical experience.
• 4 years of experience building, scaling, and debugging ML 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.

Responsibilities

• Operate across the full research-and-engineering lifecycle of frontier reasoning and agentic systems.
• Turn 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.

Skills

Deep learning

Reinforcement Learning

Post-Training

Agentic Tool-Use

Inference-Time Search

Education

Bachelor's degree in CS/Math/Physics or equivalent

PhD in CS/ML/Physics or related field

Tools

JAX

PyTorch

TensorFlow

Job description

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: London, UK; Mountain View, CA, USA; New York, NY, USA.

Minimum qualifications:

• 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.

Preferred qualifications:

• 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.

About the job

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. 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 & 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.

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, multi-step 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.

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.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.