ML Research Expert — Published First Author (Benchmark Authoring)

Domain: Machine Learning / AI Research Experience: 1+ years of hands-on ML research; no upper limit Education: Master's or PhD (in progress is fine, including current PhD candidates) You will design and validate tasks that measure what frontier AI models can actually do across machine learning. We are looking for people who write ML code and publish results, not for annotation or data-labelling experience. Task areas span: Language Models, Deep Learning, Reinforcement Learning, Vision & Generation, Robotics, ML Systems & Efficient ML, Optimization & Theory, Classical & Adaptive Learning, Time Series & Forecasting, Structured & Causal Reasoning, Trustworthy Learning, and AI for Science. Candidates who meet the requirements and provide the requested materials will be prioritised for review. As part of this process we conduct thorough background checks. Please apply only if you meet these requirements.

Responsibilities

• Design realistic ML research tasks and problem sets within your area of expertise
• Author expert-level reference solutions and grading rubrics

Requirements

• At least one first-author research paper in machine learning or a closely related field
• Master's or PhD in ML, CS, statistics, mathematics or a related quantitative field, completed or in progress
• Hands-on experience writing ML code (training, evaluation or systems work), not annotation or data labelling
• Meets the experience and education bar described in Full Description

Preferred Qualifications

• Multiple first-author publications, or papers reporting measured improvements over a baseline
• Publications at venues such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, CoRL or MLSys
• Depth in one or more of the listed task areas rather than broad familiarity

Why Apply

• Work on frontier AI research problems in your area of deep expertise
• Fully remote, asynchronous and flexible, 5 to 40 hours a week
• Competitive hourly compensation ($140-$150)