Agent Evaluation & Evolution Researcher Graduate (AML-Ark-US) - 2027 Start (PhD)

What you'll do

• Evaluate agent task success, tool use, reasoning quality, and reliability; build benchmarks and automated judging pipelines.
• Analyze execution traces and user feedback to find failure patterns and drive system improvements.
• Work with research, platform, and product teams to bring evaluation methods into production.

What they ask for

• Completing or recently completed a PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Solid foundation in machine learning and deep learning.
• Hands-on experience with LLM-based systems through research, internships, or projects.
• Strong Python skills, experience with a mainstream ML or agent evaluation framework, and demonstrated research or engineering ability.

Nice to have

• Publications at top-tier ML/NLP venues, especially in agent learning, self-improving/self-evolving/RSI, or agent evaluation.
• Experience with evaluation methodology, LLM post-training, reasoning and planning, continual learning, feedback-driven optimization, or large-scale log and trace analysis.

Tools

Python, LLM, ML

AI focus

Evaluation and improvement of LLM-based agents, including benchmarks, automated judging, trace analysis, and feedback-driven capability development.