The posting
Minimum qualifications:
- Bachelor's degree in a Life Science (e.g., Computational Biology, Chemistry), Physical Science (e.g., Physics, Materials Science), Mathematics, or Computer Science, or equivalent practical experience.
- 5 years of experience with technical programs, research engineering, or applied AI/ML projects (or 3 years with a PhD).
Preferred qualifications:
- Experience analyzing model reasoning traces on graduate-level science problems, spotting flawed assumptions or hallucinations, and translating fixes into the training pipeline.
- Experience designing lightweight, high-leverage systems for fast-moving empirical research; comfortable writing quick scripts or digging directly into datasets.
- Knowledge of SFT data quality, RL reward signals, verifier calibration, and capability tradeoffs across model sizes.
- Ability to audit individual tasks and rubrics beyond benchmark metrics.
- Ability to thrive in ambiguous, high-velocity research environments; fluent in LLMs, SFT, RL, agentic evaluations, and Python-based scientific computing stacks.
About the job
Google's projects, like our users, span the globe and require managers to keep the big picture in focus while being able to dive into the unique engineering challenges we face daily. As a Technical Program Manager at Google, you lead complex, multi-disciplinary engineering projects using your engineering expertise. You plan requirements with internal customers and usher projects through the entire project lifecycle. This includes managing project schedules, identifying risks and clearly communicating them to project stakeholders. You're equally at home explaining your team's analyses and recommendations to executives as you are discussing the technical trade-offs in product development with engineers.
Using your extensive technical and leadership expertise, you manage projects of various size and scope, identifying future opportunities, improving processes and driving the technical directions of your programs.
As a Technical Program Manager for Science & Reasoning, you will sit at the intersection of frontier AI model development and rigorous scientific domains across the life and physical sciences.
You will be in the technical weeds of model development — auditing scientific benchmarks, inspecting model reasoning trajectories and computational workflows, stress-testing RL verifiers, and shaping data and training priorities alongside research scientists and engineers.
You will drive stellar execution for our Science & Reasoning workstreams: turning research hypotheses into high-signal training data, verifiable RL environments, frontier evaluations, and deliverable model milestones across Gemini releases.
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: $217000 - $236000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Partner with research leads in life/physical sciences and formal reasoning to translate hypotheses into structured roadmaps, data mixtures, and Gemini release milestones.
- Drive end-to-end execution for human data collection, synthetic data generation, SFT, and verifiable RL training environments like computational tool-use and code execution.
- Curate and audit the evaluation suite for scientific reasoning and agentic workflows, ensuring benchmarks measure real-world scientific validity.
- Work with post-training and RL leads to analyze checkpoints, inspect reasoning traces, identify capability gaps, and translate findings into immediate training interventions.
- Manage cross-functional dependencies across data infrastructure, evaluation platforms, compute allocations, and external domain-expert vendors. Synthesize training dynamics and bottlenecks into clear launch criteria and actionable updates for tech leads.
