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Technical Program Manager, Scientific Intelligence for Gemini, DeepMind (Full Time Contract)

Google
California, USA
Manager · 5+ years experience
Onsite
USD 192000-278000 / year
Discovered 4 weeks ago
python

Full Job Posting

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.

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.

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