AI Models – Earth System

Argonne National Laboratory

Contributes to machine learning projects for weather modeling at Argonne National Laboratory. Collaborates on generative AI techniques and evaluates machine learning-based weather models.

Posted 5/29/2026full-timeLemont • Illinois • 🇺🇸 United StatesMid-LevelSenior💰 $94,486 - $147,398 per yearWebsite

ATS Keywords

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Hard Skills

machine learningdeep learningPyTorchJAXHPC systemsAI foundation modelsscientific programmingdata analysisdata assimilation techniquesnumerical computation techniques

Soft Skills

clear writingcommunicationproblem-solvingorganizational skillsflexibilityteamwork

Certifications & Qualifications

PhD Degree

Industry Keywords

geophysical sciencesatmospheric dynamicsprocess scale modelssub-seasonal-to-seasonal modelingcoupled atmosphere-ocean modelingdynamical systemscomputational toolsweather modelsgenerative AI techniquesstakeholder engagement

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

Contributes technical experience through analysis and support for programs and projects associated with machine learning, HPC, and computational problems related to earth system science and other dynamical systems.
Develops and evaluates machine learning/computational approaches, synthesis activities, computational tools, compiling results, contributes to reports, publications, and documentation.
In particular, this position will assist on projects related to applying and developing machine learning-based weather models for the S2S time frame with an emphasis on generative AI techniques, evaluating such models, and working with a team of scientists.

Requirements

What you’ll need

PhD Degree or their equivalents in geophysical sciences, computer science, machine learning, or a related field.
Experience with deep learning, PyTorch/ JAX, and scaling deep learning models to large GPU-based machines.
Experience building, training and running inferences with large AI foundation models for science domain.
Technical knowledge in using HPC systems for visualization and analysis.
Knowledge of large, dynamical systems (preferably the atmosphere), is desirable.
Skills in clear, concise writing of technical papers, and interacting and communicating effectively with colleagues.
Some problem-solving skills.
Organizational skills and flexibility in coordinating a broad spectrum of activities.
Knowledge of atmospheric dynamics, process scale models, and numerical computation techniques is preferred.
Experience in scientific programming and data analysis.
Knowledge of using atmospheric observational datasets, data assimilation techniques, and statistics is preferred.
Familiarity sub-seasonal-to-seasonal modeling and or coupled atmosphere-ocean modeling is desirable.
Ability to work and communicate with stakeholders from public and private sectors.
Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

Benefits

Comp & perks

Comprehensive benefits are part of the total rewards package.