Job Description 

Candidate will work on: 

Utilize open-source agents/models on new repositories to create more training data.
Evaluate various agents/models on existing well known SWE benchmarks.
Conduct post-training on collected training data to create fine-tuned models.
Publish findings internally or externally (e.g., open-source repositories / benchmarks, academic conferences).

Qualifications 

Currently pursuing a PhD in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, or a related field. 
Strong research background in one or more of the following: LLM training in particular Supervised Fine Tuning or Reinforcement Learning, AI agents and autonomous systems, software engineering automation, etc.
Proficient in Python or Java.
Excellent analytical, problem-solving, and communication skills. 
Experience with long-context model post-training (e.g., fine-tuning or evaluation for extended context windows) is a plus.
Publication record in top AI/ML/SE conferences

Agentic AI Research Intern

Multiple Teams