Remote ML Researcher (PhD) — AI Evaluation & Benchmarks

mercor; United States; Remote; USD 83,000 - 138,000; Part time

Job summary

Mercor seeks an experienced ML Research PhD expert for a remote contract role. You will evaluate AI-generated content, review cutting-edge ML research, and provide structured feedback to AI teams to improve model outputs and data quality.

Ideal candidates hold a PhD in ML/CS/AI, have at least one main conference paper at ICML/NeurIPS/ICLR, and preferably 2+ publications. You will work independently, meeting rolling deadlines while advancing research methodologies.

Qualifications

• PhD in ML, CS, AI or related field.
• Published at least one main conference paper at ICML, NeurIPS, or ICLR.
• Strong preference for candidates with 2+ publications at these venues.
• Demonstrated experience conducting original ML research.

Responsibilities

• Evaluate the accuracy and depth of AI-generated content to strengthen reasoning and rigor in model outputs.
• Review complex machine learning research for alignment with domain principles and methodologies.
• Provide clear, structured feedback to AI research teams to improve training data quality and downstream performance.
• Develop evaluation rubrics and benchmarks for assessing AI-generated tasks and solutions.
• Collaborate with subject matter experts to ensure consistency, relevance, and coverage across datasets.
• Work independently and asynchronously to meet deadlines while improving AI model performance.

Skills

ML research

Academic publishing

Feedback to research teams

Independent work

Education

PhD in ML/CS/AI or related field

Job description

Mercor seeks an experienced ML Research PhD expert for a remote contract role. You will evaluate AI-generated content, review cutting-edge ML research, and provide structured feedback to AI teams to improve model outputs and data quality.

Ideal candidates hold a PhD in ML/CS/AI, have at least one main conference paper at ICML/NeurIPS/ICLR, and preferably 2+ publications. You will work independently, meeting rolling deadlines while advancing research methodologies.