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.

The DOE Biological and Environmental Research (BER) program produces uniquely valuable datasets increasingly used in AI/ML, but many are not AI-ready due to inconsistent formatting, missing metadata, or incompatible file types.

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.

The successful candidate will:

• 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.

We're here for the same mission, to bring science solutions to the world. Join our team and YOU will play a KEY supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!

Why join Berkeley Lab?

We invest in our employees by offering a total rewards package you can count on:

• Exceptional health and retirement benefits, including pension or 401K-style plans
• A culture where you'll belong - we are invested in our teams!
• In addition to accruing vacation and sick time, we also have a Winter Holiday Shutdown every year.
• Parental bonding leave (for both mothers and fathers)

You will:

• Define AI-ready data standards: Establish and maintain guidance on metadata and formatting requirements for DOE environmental datasets.
• Build automated checks and tools: Develop LLM-supported methods to assess AI readiness and convert datasets into consistent, usable formats.
• Drive training and adoption: Create documentation, tutorials, and outreach to promote AI-ready data practices across the research lifecycle.
• Curate benchmark datasets: Select, standardize, and document ESS-DIVE datasets for AI training and validation.
• Support automated workflows: Contribute to developing agent-based pipelines that streamline data preparation, validation, and integration.

We are looking for:

• 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.

Material to submit:

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

Additional information:

• Application date: Priority consideration will be given to candidates who apply by June 30, 2026. Applications will be accepted until the job posting is removed.
• Appointment type: This is a full-time, 2 year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.
• Salary range: The monthly salary range for this position is $6,573 - $8,921 and is expected to start at $6,573 or above. Postdoc positions are paid on a step schedule per union contract and salaries will be predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D postdoctoral and/or related research experience.
• 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 Represented: This position is represented by a union for collective bargaining purposes.