Author complex, high-fidelity reasoning traces that document how an LLM should plan, use tools, and make decisions across sophisticated technical tasks
Design structured traces that capture multi-step problem decomposition, logical inference, and real-world decision-making strategies
Review and refine reasoning traces to ensure they reflect sound ML thinking and produce reliable, generalizable model behaviour
Develop data strategies that help LLMs navigate intricate, open-ended scenarios with greater consistency and accuracy
Contribute senior-level insights that elevate the overall quality and depth of training data
Who You Are
Experienced in machine learning, AI research, or a closely related technical field — you understand how models learn and where they go wrong
Skilled at breaking down complex problems into clear, logical, documented steps — the kind of thinking that makes great training data
Familiar with LLM evaluation, fine-tuning, or training methodologies
A precise, structured communicator who can make sophisticated reasoning legible and reproducible
Self-directed and comfortable producing high-quality work independently
Nice to Have
Prior experience with data annotation, evaluation systems, or AI quality assurance
Top-tier Kaggle competition results (Grandmaster or Master level) — a strong signal of your model intuition and problem-solving depth
Hands-on experience working with or evaluating large language models directly
Background in AI safety, interpretability, or model alignment research
Why Join Us
Work at the frontier — collaborate with world-leading AI research labs on projects that are shaping the future of machine intelligence
Fully remote and flexible — work when and where you do your best thinking
Freelance autonomy with the substance of genuinely meaningful, high-impact work
Go beyond typical ML contracting — your expertise directly influences how frontier models reason and behave
Potential for ongoing work and contract extension as new projects launch