Research Aide - DSL - Li, Xinyang - 5.21.26.
About The Position
The MAS framework studies how multiple LLM agents collaborate to solve complex tasks, and how system-level evaluation signals can be transformed into agent-level and message-level training signals for improving cooperation, reliability, and efficiency. The summer student will assist with implementing the multi-agent orchestration, logging and trace collection, evaluation hooks, and scalable execution on Aurora, enabling controlled experiments and benchmarking at leadership scale. This work aims to demonstrate one of the first end-to-end multi-agent LLM systems running natively on Aurora and contribute toward publishable results in multi-agent learning for science.
Requirements
- Currently enrolled in undergraduate or graduate studies at an accredited institution.
- Graduated from an accredited institution within the past 3 months.
- Actively enrolled in a graduate program at an accredited institution.
- Must be 18 years or older at the time the appointment begins.
- Must possess a cumulative GPA of 3.0 on a 4.0 scale.
- If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length.
- Must complete a satisfactory background check.
Responsibilities
- Assist with implementing the multi-agent orchestration.
- Assist with logging and trace collection.
- Assist with evaluation hooks.
- Assist with scalable execution on Aurora, enabling controlled experiments and benchmarking at leadership scale.
Benefits
- Comprehensive benefits are part of the total rewards package.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
No Education Listed
