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X 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 jobAt 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.
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.
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 .
