Research Associate, AI Engineer (Applied​/Software), Data Scientist

Job Details

Job Title:

Research Associate

Hiring Department:
Texas Advanced Computing Center

Position Open To:
All Applicants

Weekly Scheduled

Hours:

40

FLSA Status:
Exempt

Earliest

Start Date:

Immediately

Position Duration:
Expected to Continue

Location:

PICKLE RESEARCH CAMPUS

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.

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.

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.

• 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.

• Ph.D. in science, engineering, computer science, or related research field with a strong background in applied AI/ML and data analytics.
• Hands-on…