Instructional Faculty Positions (Open Rank)

welcoming community and contribute to it through excellence in teaching and other contributions to the academic enterprise. The department is consistently ranked among the top computer science programs in the nation. As of Fall 2026, Computer Science is one of three academic units in UT Austin's new School of Computing, alongside Statistics and Data Sciences and Information. The new School is designed to strengthen collaboration and create new opportunities for cross-departmental research and education. We are renowned for our collegial environment that fosters innovation and collaboration-an environment that attracts many top-notch faculty and excellent students from across Texas, the country, and the world. We value undergraduate education and have a committed group of highly effective educators, who are committed to helping all students reach the high standards, both technical and holistic, expected of our graduates. Our instructional faculty typically teach three courses a semester, with two of those courses usually being instances of the same course. Our class sizes tend to be on the smaller side of most of our peer departments: in Fall 2024, each faculty member taught an average of 230 students across three courses. The Department is responsive to the needs of dual career couples and supports work-life balance through an array of family-friendly policies. For more information about the department, please visit https://cs.utexas.edu . Austin, the capital of Texas, is a major technology hub, home to significant operations from companies such as Amazon, AMD, Apple, Applied Materials, AT&T, Dell, Google, IBM, Meta, NVIDIA, Samsung, Silicon Labs, and Tesla. Nestled at the edge of the Texas Hill Country, much of Austin's lifestyle is driven by outdoor activities, media, and music, due to the availability of green space, the presence of Lady Bird Lake (a consequence of a dammed river) running through town, and the location of events such as SxSW and ACL Music Festival, to name a select few. Check out our local public radio station KUT's Field Guide to Austin for a wide variety of Austin topics or the Austin Visitor Center's site for information on things to do. Position Description For this position, the primary duty is teaching undergraduate courses, which includes a wide variety of opportunities, though teaching in our in-person and online graduate programs may be possible for some candidates. While these positions are not eligible for tenure, they have opportunities for advancement, and we hire with the intent that new instructional faculty will become a lasting part of our community. Teaching Opportunities Teaching opportunities range from introductory computer science courses, such as data structures, discrete mathematics, and computer architecture, to advanced courses, particularly topics related to artificial intelligence, computer systems, and UI/UX. We have opportunities to teach classes dedicated to CS majors as well as classes targeted to majors from other disciplines-many of our instructional faculty teach both types of classes. For the 2026-2027 cycle, we are particularly interested in candidates that can teach artificial intelligence and related areas, as well as introductory computer science topics. Our curriculum offers students an opportunity to investigate the social impact of computer science, and so we have opportunities to develop and teach classes that are technical with social impact considerations integrated into the content as well as stand alone technical courses and those related to social impact and ethics. Finally, we are launching interdisciplinary degrees, and so we will have opportunities to teach capstone courses in the integrated disciplines. In Fall 2025, we welcomed our first students in our History+CS, Linguistics+CS, and Neuroscience+CS programs. In our department, faculty teaching large courses are supported by Graduate Teaching Assistants (TA), Undergraduate Course Assistants (UGCA), or a combination of the two. We find that our TAs and UGCAs are motivated and enjoy helping students, and many of our instructional faculty develop strong collaborations and close mentoring relationships with their TAs and UGCAs. As part of the position, faculty who are assigned TAs or UGCAs are responsible for assigning and overseeing their duties. Applicants able to teach any of the aforementioned topics are encouraged to apply. Our faculty work together to provide the courses the students need. We expect deep expertise in each faculty member's subject area and value a willingness to teach related topics or introductory courses as course needs change. Additional Contributions to the Academic Enterprise In addition to teaching, instructional faculty at UT Austin are also expected to contribute to the academic enterprise through active service, research, mentoring, or any combination of the three. Each faculty member creates their own unique set of contributions based on interest, skill sets, and need, but we do expect our instructional faculty to spend some of their time encouraging and mentoring students towards careers in industry, government, and the academy, as well as developing and improving the curriculum within their courses. Instructional faculty have the opportunity to contribute through committee service, too, as they serve on and chair committees along with other faculty in the department. Qualifications • A Master's degree in Computer Science or related discipline, including a depth of knowledge in the expected teaching areas is required. A Ph.D. in Computer Science or a related discipline is preferred. • Strong communication skills are necessary, particularly an ability to clearly communicate complicated technical concepts • A willingness to collaborate and foster community is required, as we are a collegial and collaborative department, and we hope you will join us in our mission to maintain that environment. • Previous teaching experience is not required, but it is helpful. Application Instructions All faculty positions require a cover letter, current curriculum vita, teaching statement and three (3) professional reference letters. Letters of reference must address the candidate's overall quality of teaching and/or presentation skills, the ability to communicate complex topics, and expertise in the field of computer science. As part of their teaching statement, applicants are encouraged to include examples of effective pedagogy they use or plan to use in their classroom, including how they engage or plan to engage students. This description may include activities or strategies they employ or plan to employ. For applicants with teaching experience, please include sample course materials and assignments; peer or student evaluations; and any descriptions of pedagogical innovations or continuous improvements that have been employed in your courses. Please submit those materials as a single file with your teaching statement. Applicants who desire confidentiality should explicitly mention this in the first paragraph of their cover letter. We will consider applications in two rounds, with deadlines of November 15, 2026, and February 1, 2027. Any applications received after those dates may be considered until the positions are filled. The expected start date is Fall 2027, though other possibilities will be considered. Inquiries may be directed to faculty-search@cs.utexas.edu . The University of Texas is an Equal Opportunity Employer. Equal Employment Opportunity Statement 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. To apply, visit https://faculty.utexas.edu/career/192895 je-38ff312574914b7ba2b46e063e87d0a3