Agent Evaluation & Evolution Machine Learning Engineer Graduate (AML-Ark-US) - 2027 Start

What you'll do

• Design agent evaluation systems for task success, tool use, reasoning quality, and reliability.
• Build benchmarks and automated judging pipelines using rule-based checks, model-based judging, and human review.
• Analyze execution traces and user feedback, then collaborate with research, platform, and product teams to improve and productionize methods.

What they ask for

• Completing or recently completed a Bachelor's/Master's degree 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-evolving systems, 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

AI focus

Evaluates and improves LLM-based agents through benchmarks, automated and human judging, trace analysis, and feedback-driven production improvements.