Science Expert Lead, Gemini Data, DeepMind

Google; New York (NY); Hybrid; USD 207,000 - 300,000; Full time

Job summary

Google DeepMind is expanding Gemini’s frontier AI capabilities for Science professionals. We seek a Science Expert Lead to shape foundational data strategy, map complex science workflows, and curate high-quality data to make Gemini the AI assistant for science experts.

You will work in a role that bridges scientific research and AI, prioritizing data coverage, model enhancement, and cross-functional collaboration across locations in the US such as New York and Mountain View.

Qualifications

• Master's degree in natural science or equivalent practical experience.
• Experience measuring LLMs or generative AI for scientific use cases.

Responsibilities

• Set and map the Science data strategy to prioritise capabilities and data sourcing.
• Design tasks and rubrics; QA datasets from vendors and synthetic pipelines.
• Analyze model failures and stress-test checkpoints to identify gaps in knowledge.
• Develop science evaluations and advise cross-team efforts on coverage and realism.
• Scale expertise by guiding SMEs and converting contributions into training data.

Skills

LLM evaluation

Cross-disciplinary collaboration

AI model evaluation

Scientific data curation

Education

Master's degree in natural science

Job description

Science Expert Lead, Gemini Data, DeepMind

New York, NY, USA; Mountain View, CA, USA

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

Minimum qualifications:

• Master's degree in Natural Science (e.g., Biology, Chemistry, Physics), computational science, or equivalent practical experience.


• Experience measuring the performance of Large Language Models (LLMs) or generative AI tools and optimizing them for scientific use cases.

Preferred qualifications:

• Experience breaking down laboratory or computational workflows into concrete tasks, evaluation rubrics, and quality standards.


• Experience prioritizing AI capabilities and collaborating closely with cross-functional technical partners to unlock value for scientists.


• Demonstrated dual expertise bridging scientific research and AI, such as transitioning from a scientist into a technical machine learning role, or vice versa.


• Strong technical capabilities evaluating machine learning models, and applying Large Language Models to complex scientific workflows.

About the job

At Google DeepMind, we are expanding Gemini’s frontier AI capabilities into highly specialized professional domains. We are seeking a Science Expert Lead to shape our foundational data strategy for the financial sector. In this role, you will apply your deep domain knowledge to map complex Science workflows, identify critical areas for model enhancement, and curate the high-quality data necessary to make Gemini the ultimate AI assistant for Science professionals.

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.

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

• Set the Science data strategy by mapping workflows into a use-case taxonomy, and partnering with research to prioritize capabilities, data coverage, and sourcing pipelines.


• Create high-quality Science data by designing tasks and rubrics, and QA-ing datasets from vendors, customers, and synthetic pipelines for correctness, realism, and coverage.


• Steer data priorities using model failures by reproducing reported losses and stress-testing checkpoints on real workflows to identify missing scientific knowledge and false assumptions.


• Build Science evaluations by assessing industry benchmarks to measure data strategy success, and advise cross-team evaluation efforts on scientific coverage and realism.


• Scale expertise by recruiting and guiding SMEs, acting as the domain authority to enforce vendor quality, and converting expert contributions into usable training data.

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