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Machine Learning Postdoc Jobs in Texas (NOW HIRING)

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Machine Learning Postdoc information

See Texas salary details

$19.3K

$100.3K

$188.6K

How much do machine learning postdoc jobs pay per year?

As of Sep 6, 2026, the average yearly pay for machine learning postdoc in Texas is $100,252.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,007.00 and $138,071.00 per year, depending on experience, location, and employer.

What is a machine learning postdoc?

A Machine Learning Postdoc is a research-focused position typically held after earning a Ph.D. in a related field. It involves conducting advanced research in machine learning, developing new algorithms, and publishing in top-tier conferences and journals. Postdocs often collaborate with faculty, industry partners, and other researchers to advance the state of the art in AI. The role may include mentoring students and contributing to grant proposals. It serves as a bridge between doctoral studies and a long-term academic or industry research career.

What are the typical responsibilities and collaborative aspects of a machine learning postdoc?

A Machine Learning Postdoc typically conducts original research, develops and tests new algorithms, and contributes to academic publications or patent applications. Daily tasks often involve data analysis, model building, and experimentation using advanced computational tools. Collaboration is key in this role, as postdocs frequently work alongside faculty, graduate students, and external industry partners to advance research objectives. Additionally, they may mentor junior researchers or students, present at conferences, and participate in grant writing or project planning. This mix of independent research and team collaboration fosters both professional growth and impactful scientific advancements.

What are the key skills and qualifications needed to thrive in a machine learning postdoc position?

To thrive as a Machine Learning Postdoc, you need a deep understanding of machine learning algorithms, statistical modeling, and research methodology, typically supported by a completed PhD in a related field. Proficiency with programming languages like Python or R, experience with ML libraries (e.g., TensorFlow or PyTorch), and familiarity with large-scale datasets and cloud computing platforms are important. Strong analytical thinking, effective communication, and the ability to collaborate across multidisciplinary teams are standout soft skills in this position. These qualifications ensure innovative research contributions, successful project execution, and effective dissemination of findings in both academic and applied settings.

What are the most commonly searched types of Machine Learning Postdoc jobs in Texas?

The most popular types of Machine Learning Postdoc jobs in Texas are:

What cities in Texas are hiring for Machine Learning Postdoc jobs?

Cities in Texas with the most Machine Learning Postdoc job openings:

Infographic showing various Machine Learning Postdoc job openings in Texas as of August 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 77% In-person, 7% Hybrid, and 16% Remote job distribution, with an average salary of $100,252 per year, or $48.2 per hour.

$70K/yr

Full-time

Re-posted 4 days ago


University Of Texas at Austin rating

8.3

Company rating: 8.3 out of 10

Based on 64 frontline employees who took The Breakroom Quiz

127th of 631 rated colleges and universities


Job description

Job Posting Title:
Postdoctoral Fellow
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Hiring Department:
Department of Marine Science
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Position Open To:
All Applicants
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Weekly Scheduled Hours:
40
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FLSA Status:
Exempt from FLSA
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Earliest Start Date:
Jul 31, 2026
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Position Duration:
Expected to Continue Until Jun 30, 2027
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Location:
AUSTIN, TX
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Job Details:
General Notes
The University of Texas at Austin's Department of Marine Science and the Marine Science Institute, in collaboration with the Department of Computer Science's Computer Vision group, invite applications for an exciting postdoctoral position at the intersection of marine ecology, ocean technology, machine learning, and high-throughput biological imaging.
This position offers a rare opportunity to help pioneer the use of advanced shadowgraph imaging systems for studying the transport and behavior of plankton and larval organisms in dynamic and estuarine environments.
Purpose
The successful candidate will help develop and validate AI-driven pipelines for the detection, segmentation, classification, and tracking of zooplankton and estuarine-dependent larvae in noisy, high-volume imaging datasets collected under challenging field conditions. The work will involve applying state-of-the-art machine learning approaches to classify organisms despite cluttered imagery, marine snow, suspended particles, and subtle morphological differences among taxa.
This position will be located in Austin, TX
Responsibilities
  • Developing automated image-analysis workflows; implementing visual validation protocols
  • Exploring active learning and crowdsourced annotation approaches
  • Quantifying organism movement and behavior through image tracking
  • Advancing unsupervised and semi-supervised classification methods to discover previously unrecognized biological groupings
  • Explore adaptive AI-guided imaging strategies, including reinforcement-learning approaches that dynamically optimize sampling based on environmental conditions such as turbidity and currents
  • Lead the preparation of manuscripts for submission to high-impact peer-reviewed conferences and journals
  • Contribute to the intellectual development of students and junior researchers

Required Qualifications
  • PhD in Computer Science or Electrical Engineering received within the last 3 years.
  • Essential experience includes Python, modern techniques in visual recognition and image segmentation, and the ability to rapidly prototype and fine-tune open-source computer vision models

Preferred Qualifications
Candidates with expertise in computer vision, AI/machine learning, and data science are encouraged to apply.
Salary Range
$70,000
Working Conditions
  • Most work will take place in a typical office environment
  • Periodic trips will be made from Austin to the Marine Science Institute in Port Aransas to work with Dr. Sharon Herzka (Department of Marine Science) and other project collaborators. Travel will require driving a personal vehicle.

Required Materials
  • Resume/CV
  • 3 work references with their contact information; at least one reference should be from a supervisor
  • Letter of interest

Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. 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 UT Jobs. If you are a current University employee, 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 before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.
Employment Eligibility:
Please make sure you meet all the required qualifications and you can perform all of the essential functions with or without a reasonable accommodation.
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.
Background Checks:
A criminal history background check will be required for finalist(s) under consideration for this position.
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.
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.
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:
  • E-Verify Poster (English and Spanish) [PDF]
  • Right to Work Poster (English) [PDF]
  • Right to Work Poster (Spanish) [PDF]

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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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