Job Posting Title:
Research Associate
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Hiring Department:
Texas Advanced Computing Center
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Position Open To:
All Applicants
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Weekly Scheduled Hours:
40
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FLSA Status:
Exempt
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Earliest Start Date:
Immediately
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Position Duration:
Expected to Continue
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Location:
PICKLE RESEARCH CAMPUS
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Job Details:
Job Description
The Scalable Computational Intelligence (SCI) group is a team of researchers and engineers who develop and apply AI/ML techniques to solve challenging problems in science and engineering. The Research Associate will work in the SCI group to support researchers in leveraging modern AI/ML methods to accelerate scientific discovery and innovation in various domain areas. The ideal candidate will have a strong background in data analytics, AI/ML, and a demonstrated passion for applying emerging AI/ML methods across diverse science and engineering domains.
General Notes
The Texas Advanced Computing Center (TACC) at The University of Texas at Austin is one of the leading supercomputing centers in the world, supporting advances in computational research by thousands of researchers and students. TACC staff help researchers and educators use advanced computing, visualization, and storage technologies effectively, and conduct research and development to make these technologies more powerful, more reliable, and easier to use. TACC staff also educate and train the next generation of researchers, empowering them to make discoveries that advance knowledge and change the world.
The Texas Advanced Computing Center fosters a culture of innovation, passion, and fun by encouraging staff members to actively collaborate to investigate the latest technologies, team up for charities, and celebrate successes together. TACC promotes a healthy workplace by helping employees achieve balance between their personal and professional lives to increase employee engagement, job satisfaction, and overall well-being.
If you are not sure that you’re 100% qualified, but up for the challenge – we want you to apply. We believe skills are transferable and passion for our mission goes a long way.
Candidates will need to upload a resume, letter of interest, unofficial copy of transcript, and the names of three references to apply for this position.
UT Austin offers a competitive benefits package that includes:
- 100% employer-paid basic medical coverage
- Retirement contributions
- Paid vacation and sick time
- Paid holidays
Please visit our Human Resources (HR) website to learn more about the total benefits offered.
Purpose
The Research Associate will train, evaluate the performance of and/or run inference on AI/ML models on TACC’s systems, assist users in leveraging TACC’s compute resources in their ML pipelines, mentor staff, meet with collaborators to discuss emerging techniques, and/or contribute to technical reports or funding proposals. Work is highly collaborative and interdisciplinary, requiring both independent technical contributions and active engagement with researchers across diverse scientific and engineering domains.
Responsibilities
- Consult and collaborate with data providers, analysts, systems experts, and research staff to design, develop, and deploy advanced AI/ML systems for defined project requirements.
- Mentor TACC staff in machine learning, data analytics, and emerging methods (e.g., prompt engineering, workflow orchestration with AI agents, deployment on HPC systems, etc.).
- Support the application of AI/ML across a diverse range of scientific domains.
- Support training of AI/ML techniques and best practices to a broad range of researchers
- Collaborate and propose new funding opportunities supporting research done at TACC.
- Prepare reviewed papers, technical reports, design, and requirements of data analytic techniques and systems, optimizations, and novel applications across domains supported at TACC.
- Stay at the forefront of new techniques and technologies applicable to AI/ML systems that support implementations in various science and engineering domains.
- Perform other related functions as assigned.
Required Qualifications
- Ph.D. in science, engineering, computer science, or related research field with a strong background in applied AI/ML and data analytics.
- Hands-on experience with AI/ML techniques and platforms.
- Demonstrated experience working with researchers and domain experts to deliver data analytics or machine learning solutions.
- Ability to quickly learn and adapt new technologies—especially emerging AI tools, platforms, and frameworks.
- Excellent written and verbal communication skills.
Relevant education and experience may be substituted as appropriate.
Preferred Qualifications
- Experience with large language models (LLMs), multimodal ML, and/or agentic AI to automate, optimize, and advance scientific and engineering research workflows.
- Experience in developing or applying surrogate modeling to accelerate simulations or approximate complex physical processes.
- Experience with supporting and extending open-source and open-data products for research communities
- Experience analyzing both measured and simulated data sources.
- Experience training and mentoring researchers in data workflows and best practices for incorporating AI/ML methods.
- Strong problem-solving and strategic-thinking skills, with the ability to translate emerging AI technologies into practical solutions for science and engineering.
Salary Range
$90,000 + depending on qualifications
Working Conditions
- Typical Office Environment
- Repetitive use of a keyboard
Required Materials
- Resume/CV
- Letter of interest
- 3 work references with their contact information; at least one reference should be from a supervisor
- Unofficial copy of transcript
Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume in the first step of the online job application process. Then, any additional Required Materials will be uploaded in the My Experience section; you can multi-select the additional files or click the Upload button for each file. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.
Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find Jobs. Before you apply though, log-in to Workday, navigate to your Worker Profile, click the Career link in the left-hand navigation menu and then update the sections in your Professional Profile. This information will be pulled in to your application. The application is one page and you will need to click the Upload button multiple times in order to attach your Resume, References and any additional Required Materials noted above.
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Employment Eligibility:
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.
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Retirement Plan Eligibility:
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. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length.
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Background Checks:
A criminal history background check will be required for finalist(s) under consideration for this position.
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Equal Opportunity Employer:
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.
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Pay Transparency:
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.
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Employment Eligibility Verification:
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.
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E-Verify:
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:
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Compliance:
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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