AI Computational Scientist – Senior Software Engineer
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Job summary
The University of Texas at Austin invites applications for an AI Computational Scientist – Senior Software Engineer to advance AI/ML in Earth and planetary sciences. Located within the Jackson School of Geosciences, this role collaborates across units to build scalable AI-enabled workflows and deploy novel computational methods.
The position emphasizes coordination, teaching, and strategic AI development, with an expected start date immediately and a 40-hour work week on UT's main campus in
Qualifications
- Master's degree in a related field (Earth sciences, computational sciences, CS, engineering, physics)
- At least three years of software development on complex systems
- Experience with Python and scientific Python
- Experience training/evaluating ML models with PyTorch, JAX or TensorFlow
- Proficiency with Linux-based HPC environments and version control
- Strong communication and ability to work with multidisciplinary teams
Responsibilities
- Partner with researchers to accelerate AI/ML research in geosciences
- Design and evaluate ML workflows for Earth and planetary science
- Develop neural surrogates, graph networks, physics-informed networks
- Collaborate with units to adopt advanced AI methods
- Develop, test, and debug scientific software and workflows
- Mentor staff on AI-assisted software development and tools
Skills
Education
Tools
Job description
John A and Katherine G Jackson School of Geosciences
All Applicants
40
Exempt from FLSA
Immediately
Expected to Continue
UT MAIN CAMPUS
The AI Computational Scientist will provide 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. The position is housed in the Office of the Dean and reports to the Special Advisor to the Dean for AI and Computing. It complements and supports the existing computational scientists in the units.
- Partner with faculty, research scientists, postdoctoral scholars, graduate students, and unit computational scientists across the Jackson School of Geosciences to accelerate research through the application of artificial intelligence and machine learning methods.
- Design, implement, and evaluate machine learning, scientific AI, and data integration workflows for Earth and planetary science research 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.
- Collaborate directly with research groups through embedded engagements to enhance 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.
- Collaborate with unit computational staff to provide guidance on AI-assisted software development, including evaluation, verification, effective use, and limitations of code-generation tools.
- Curate and maintain shared resources, including inventories of AI-ready geoscience datasets, benchmark problems, and reference implementations for common scientific AI applications.
- Collaborate with faculty to develop computational learning materials, including notebooks, datasets, exercises, and instructional modules that introduce artificial intelligence and machine learning concepts in 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 adoption 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 related to 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. This position does not administer shared computing infrastructure, provide general information technology support, or manage generative AI model training programs.
- 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 frameworks such as 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.
- Demonstrated ability to work independently, manage multiple priorities, and meet established deadlines.
- Excellent oral, written, and interpersonal communication skills, with the ability to collaborate effectively with multidisciplinary research teams.
- Ph.D. in Earth sciences, computational sciences, applied mathematics, computer science, engineering, physics, or a related field.
- Demonstrated 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 additional programming languages beyond Python.
$95,000 + depending on qualifications
- May work around standard office conditions
- Repetitive use of a keyboard at a work station.
- Use of manual dexterity.
- Weekend and evening work may be required.
- Lifting objects, bending, kneeling, walking, standing.
- Resume/CV
- 3 work references with their contact information; at least one reference should be from a supervisor
- Letter of interest
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Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval.
The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length.
A criminal history background check will be required for finalist(s) under consideration for this position.
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.
The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:
- E-Verify Poster (English and Spanish) [PDF]
- Right to Work Poster (English) [PDF]
- Right to Work Poster (Spanish) [PDF]
Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.
The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.
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