AI Computational Scientist – Senior Software Engineer

University of Texas at Austin

13d•Onsite

About The Position

The AI Computational Scientist provides technical expertise and coordination to advance the use of artificial intelligence and machine learning in Earth and planetary science research across the Jackson School of Geosciences and its three units: the Department of Earth and Planetary Sciences, the Institute for Geophysics, and the Bureau of Economic Geology. Housed in the Office of the Dean, the position complements and supports the computational scientists in those units and reports to the Special Advisor to the Dean for AI and Computing. The role is not eligible for employer sponsorship. The position partners with researchers to design and evaluate scientific AI methods and workflows, supports computational teaching and training, and coordinates AI-related opportunities across the school. It may involve standard office conditions, repetitive keyboard use, and manual dexterity. Weekend and evening work may be required, as may lifting objects, bending, kneeling, walking, and standing. This position does not administer shared computing infrastructure, provide general IT support, or manage generative AI model training programs.

Requirements

Master's degree in Earth sciences, computational sciences, applied mathematics, computer science, engineering, physics, or a related field.
At least three years of experience in software development on complex systems.
Production experience with at least two programming languages, including scientific Python.
Demonstrated experience developing, training, and evaluating machine learning models using PyTorch, JAX, TensorFlow, or similar technologies.
Experience with Linux-based scientific computing environments and high-performance computing systems.
Experience using version control systems and collaborative software development practices.
Ability to work independently, manage multiple priorities, and meet established deadlines.
Excellent oral, written, and interpersonal communication skills, including the ability to collaborate effectively with multidisciplinary research teams.
Relevant education and experience may be substituted as appropriate.

Nice To Haves

Ph.D. in Earth sciences, computational sciences, applied mathematics, computer science, engineering, physics, or a related field.
Experience with scientific machine learning methods, including neural surrogates or emulators, physics-informed neural networks, operator learning, or related approaches.
Research experience in Earth and planetary sciences or significant experience working with geoscience datasets.
Experience developing computational training materials, instructional modules, workshops, or course content.
Experience integrating and managing heterogeneous scientific datasets and data formats.
Familiarity with cloud computing platforms and advanced computing resources, including the TACC ecosystem and related research computing infrastructure.
Record of peer-reviewed scholarly publications and/or contributions to open-source software projects.
Proficiency in programming languages beyond Python.

Responsibilities

Partner with faculty, research scientists, postdoctoral scholars, graduate students, and unit computational scientists to advance research through AI and machine learning.
Design, implement, and evaluate machine learning, scientific AI, and data integration workflows for Earth and planetary science applications.
Develop and apply scientific AI approaches, including neural surrogates and emulators, graph-network-based simulators, physics-informed neural networks, and foundation-model-based tools for geoscience data.
Work with research groups through embedded engagements to improve computational workflows, transfer technical expertise, and support adoption of advanced AI methods.
Develop, review, test, and debug scientific software and computational workflows in partnership with researchers.
Guide unit computational staff on AI-assisted software development, including evaluation, verification, effective use, and limitations of code-generation tools.
Curate and maintain shared inventories of AI-ready geoscience datasets, benchmark problems, and reference implementations for common scientific AI applications.
Collaborate with faculty to develop computational learning materials such as notebooks, datasets, exercises, and instructional modules for undergraduate and graduate geoscience courses.
Support workshops, training activities, and educational initiatives focused on AI methods, scientific computing, and AI-assisted programming.
Provide technical expertise and consultation to promote responsible and effective use of AI technologies in educational settings.
Collaborate with the Dean's Office, academic units, and university partners to identify and advance AI-related opportunities across the Jackson School of Geosciences.
Represent the school in university-level technical AI working groups and communicate opportunities concerning computing resources, software licensing, seed funding, and related initiatives.
Work with Dean's staff to evaluate, develop, and help implement administrative AI approaches to facilitate workflows and coordinate technology transfer and training for JSG staff.
Track, document, and report on AI-related activities, needs, and emerging opportunities across the school.
Perform other related duties as assigned.