Implement basic imitation learning algorithms such as supervised finetuning (SFT) and evaluate them in benchmark reinforcement learning environments.Implement an reinforcement learning algorithm using the outcomes of tasks 1 and 2, and prepare evaluation reports for this algorithm in benchmark reinforcement learning environments.Maintaining source code of the training pipelines, such as documentation of the source code, preparing usage manuals, and identification of library dependencies.Collaborate with internal departments to share information and improve data handling processes.Knowledge, Skills, and Abilities- Skilled in maintaining code for libraries, APIs, deep learning, and reinforcement learning pipelines.
- Ability to generate evaluation plots on the performance of algorithms, and visualize the reasoning traces of Monte-Carlo Tree Search agents
- Ability to translate technical concepts into clear documentation and collaborate across multidisciplinary teams.
This job posting reflects the general nature and level of work expected of the selected candidate(s). It is not intended to be an exhaustive list of all duties and responsibilities. The institution reserves the right to amend or update this description as organizational priorities and institutional needs evolve.Minimum Qualifications- Bachelor's degree or equivalent advanced learning attained through experience required.
- 1 year of relevant work experience is required.
FLSA: Exempt
Full Time/Part Time: Full Time
Number of Hours Worked per Week: 40
Job FTE: 1.0
Work Calendar: Fiscal
Job Category: Research
Benefits Eligible: No Benefits
Rate of Pay: $47,356-$59,195
Compensation Type: salary at 1.0 full-time equivalency (FTE)
Grade6
Compensation GuidanceThe
Rate of Pay Field represents the University of Arizona's good faith and reasonable estimate of the range of possible compensation at the time of posting. The University considers several factors when extending an offer, including but not limited to, the role and associated responsibilities, a candidate's work experience, education/training, key skills, and internal equity.
The
Grade Range represent a full range of career compensation growth over time. The university offers compensation growth opportunities within its career architecture. To learn more about compensation, please review our
Applicant Compensation Guide and our
Total Rewards Calculator.
Career Stream and LevelPC1
Job FamilyResearchers & Scientists
Job FunctionResearch
Type of criminal background check required: Name-based criminal background check (non-security sensitive)
Number of Vacancies: 1
Expected End Date: 12/18/2026
Contact Information for CandidatesChicheng Zhang Assistant Professor
chichengz@arizona.eduOpen Until Filled: Yes
Documents Needed to Apply: Resume and Cover Letter
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