Senior Physics Adjudicator (AI Training)

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help people discover specialized AI training projects, build a unified portfolio of annotation and adjudication work, and grow a durable freelance career in the human side of AI.

We connect experts and contributors with meaningful tasks that directly shape how state-of-the-art AI systems behave — while supporting remote, flexible, project-based work that fits around research, teaching, or other commitments.

About AI training and adjudication work

AI training (data labeling/annotation and human evaluation) is how humans teach models to reason, reason about evidence, and produce reliable outputs. Adjudicators provide definitive scientific judgments where opinions or methods conflict, improve dataset quality, and help models handle edge cases.

This role focuses on document-level review and evaluation: assessing technical answers, resolving methodological disagreements, and editing model outputs so they meet rigorous research standards.

The role

We are hiring a Senior Physics Adjudicator to provide authoritative review and judgment on advanced physics material used for AI training. This contractor, part-time position calls for an active research profile and demonstrated subject-matter authority.

You will work across technical documents and model responses — evaluating competing approaches, resolving disputes in interpretation, and producing detailed feedback that improves training data and model behavior.

• Employment type: Contractor, part-time.
• Work involves document review, evaluation ratings, and text-generation style editing.
• Work is remote; preferred candidates based in US, GB, or CA (exceptional candidates from other locations may be considered).
• Primary language: English.

What you'll do

You will apply research judgment to adjudicate advanced physics problems, refine technical content, and provide clear, actionable feedback for model training and dataset improvement.

• Adjudicate advanced physics problems within your subfield and resolve conflicting interpretations.
• Evaluate and compare methodologies, results, and reasoning used in model outputs.
• Review and refine scientific text to align with strong research standards and clarity.
• Provide detailed written and verbal feedback that improves model outputs and dataset quality.
• Collaborate with a global team of experts to strengthen training datasets and edge-case handling.
• Bring a rigorous, research-led perspective to nuanced scientific questions and edge cases.

Requirements

All required qualifications come from the project brief and will be validated during review and onboarding.

• PhD in physics or senior PhD student status with ongoing research in a relevant subfield.
• 2–5 representative publications from the past 5 years, available via arXiv or DOI.
• Experience as a professor, industry research PI, or equivalent subject-matter authority.
• Strong scientific judgment for evaluating edge cases and resolving disagreements.
• Broad cross-disciplinary awareness and familiarity with alternative research standards.
• Leadership experience mentoring, reviewing, or directing research projects or labs.
• Proficiency in LaTeX, SymPy, Python, and Jupyter.

Helpful background and time commitment

Helpful experience includes adjudicating research disputes, peer-reviewing high-impact publications, or working on AI, machine learning, or computational modeling projects. These are desirable but not strictly required.

The project brief lists an expected engagement of approximately 10 hours per week over an 8–10 week period. Note: the structured time field in the listing indicates '20+ hours/week' — please be aware of this discrepancy and confirm expected weekly hours during screening.

• Hiring focus areas: high energy physics, mathematical physics, biophysics, statistical physics, condensed matter, AMO and quantum optics, gravitation and cosmology, quantum information, and optical properties of materials.
• Availability: the description suggests ~10 hours/week; confirm during interview given the conflicting structured time field.
• Preferred locations: US, UK, CA; exceptional candidates from other countries may be considered.

Compensation and how to apply

Pay is contractor hourly at USD 80–110 per hour (listing shows an upper rate of $110/hr). This is a paid, short-term project-based engagement.

To apply, create a profile on OpenTrain, provide your publication links (arXiv/DOI), a brief CV highlighting mentoring or PI experience, and note your availability. Applications will be reviewed for research fit and demonstrated publication record.

• Labeling focus: document review with evaluation ratings and text-generation editing.
• Expected contract length: roughly 8–10 weeks, with part-time hours as noted above.