AI-Readiness & Data Automation Postdoctoral Scholar

Job Summary

The Earth and Environmental Sciences Area at Lawrence Berkeley National Laboratory (LBNL) seeks a postdoctoral researcher to develop and curate unique and cutting-edge AI-ready data for the U.S. Department of Energy’s ESS-DIVE repository.

About the Role

The selected candidate will join an interdisciplinary team to improve and expand upon how DOE environmental data is prepared for AI to further our understanding of Earth system processes and to enable environmental management. This includes working with ESS-DIVE users and the broader community to create machine-readable data products and develop tools and guidance for contributors.

Responsibilities

• Develop practical guidance for what “AI-ready data” should include that extends beyond the FAIR (Findable, Accessible, Reusable, Interoperable) principles
• Build and extend tools that validate datasets, and check whether they meet AI-readiness requirements.
• Help automate dataset preparation using reporting format templates and structured workflows.
• Enable translation of legacy DOE data into AI-ready formats, lead creation of example AI-ready benchmark datasets and supporting documentation.

Qualifications

• Ph.D. in environmental science, earth science, informatics, or a closely related field.
• Experience working with environmental/scientific datasets (cleaning, processing, analysis, synthesis).
• Strong programming skills, especially Python (or comparable scientific programming).
• Experience with LLM-assisted or agent-based workflows.
• Strong written and oral communication skills, including the ability to explain technical requirements to non-experts.
• Demonstrated record of scholarly or technical contributions (e.g., publications, reports, or significant software contributions).

Desired Skills/Knowledge

• Experience with metadata standards, data schemas, or FAIR principles, particularly with data formats commonly used in earth/environmental sciences (e.g., netCDF).
• Experience building data pipelines for ingesting and harmonizing data from multiple sources and tracking data provenance.
• Familiarity with agentic AI tooling such as Retrieval Augmented Generation (RAG) pipelines, agent skills, and Model Context Protocol (MCP) servers.
• Ability/willingness to travel to partner institutions and conferences as needed.

Application Materials

• CV and cover letter, including a list of recent publications
• Links to any relevant public code repositories if available

Background Check

This position is subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.

Work Modality

Work will be primarily performed at: Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. A REAL ID or other acceptable form of identification is required to access Berkeley Lab sites (for more information click here).

Union Representation

This position is represented by a union for collective bargaining purposes.