Technical Program Manager, Scientific Intelligence for Gemini, DeepMind

The Student Researcher Program fosters academic collaborations by hiring students onto research projects aligned to company priorities in scientific advancement. The program offers placements on teams across Google for research, developing, and science roles. As a Student Researcher, you will have the opportunity to participate in research projects focused on developing solutions for real-world, large-scale problems. Student Researcher projects are exploratory and experiences that drive scientific advancement across a multitude of research areas. Students will work collaboratively on projects that explore innovative research challenges and support the creation of breakthrough technologies. Projects vary in duration and location based on team and student requirements. It is required that you are located in one of the specific country locations identified for this role for the full duration of the engagement. When you apply, you will be considered for Student Researcher positions across all of Google's research teams, including DeepMind, Google Research, Google Cloud and more. This allows us to find the right project match for your skills and interests. Academic Level: While the Student Researcher Program is open to students at all degree levels, the majority of our projects are designed for PhD talent. We actively welcome exceptional researchers pursuing a Bachelor’s or Master’s degree in Computer Science or a related field; however, please note that these opportunities are available in a highly limited capacity and require a demonstrated research focus or a strong publication record. Program Distinction: Unlike the Research Internship, which is a structured seasonal placement, the Student Researcher role offers greater flexibility in weekly time commitment, project duration, and onsite/remote options. Specific work arrangements and engagement timelines are confirmed during the offer stage. Researchers across Google are working to advance the computing and build the next generation of intelligent systems for all Google products. To achieve this, we invest in foundational research and work on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Whether we're shaping the future of sustainability, optimizing algorithms, or pioneering AI systems, our teams strive to continuously progress science, advance society, and improve the lives of billions of people.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

[Bachelor's degree] Canada: $99000 (CAD) + 0% bonus target [Master's degree] Canada: $102000 (CAD) + 0% bonus target

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.

Qualifications 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.