Physical Sciences Research Assistant

AI Trainer Jobs; United States; Remote; USD 136,000 - 194,000; Full time

Job summary

AI Trainer Jobs is seeking a remote Physical Sciences Research Assistant to review AI outputs in physics, validate derivations, and document correct methods for training data curation. You will assess reasoning, reproduce key steps, and flag errors to ensure robust model training and reliable results.

The role requires graduate-level physics expertise, strong analytical writing, and the ability to work asynchronously for at least 10 hours per week as an independent contractor.

Qualifications

• Graduate-level training or equivalent in physics or related field.
• Experience publishing, teaching, or advising at a professional level.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning citing methods, papers, or worked examples.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current physics methods, conventions, and prior work for Physical Sciences Research Assistant assignments.
• Reproduce or sanity-check key derivations, calculations, or experimental claims.
• Flag dimensional, methodological, and citation errors with structured severity tags.
• Capture the corrected reasoning or worked example so the modeling team can train on it.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Physics

Education

PhD or equivalent research experience

Job description

Physical Sciences Research Assistant is a remote review track for evaluating AI outputs across physics reasoning, calculations, and research workflows. Reviewers grade derivations and assumptions, reproduce key results, and document the correct method so the modeling team can train on it.

Category: Scientific AI & Domain Experts · Pay: $120 / hr · Location: Remote - US-eligible · Contractor

Physical Sciences Research Assistant is a remote review track for evaluating AI outputs across physics reasoning, calculations, and research workflows.

About the role

Physical Sciences Research Assistant is a remote review track for evaluating AI outputs across physics reasoning, calculations, and research workflows. Reviewers grade derivations and assumptions, reproduce key results, and document the correct method so the modeling team can train on it.
Physics models live or die on whether their derivations actually hold up under scrutiny. AuraOne uses scientific specialists to grade outputs the way a peer reviewer would - checking assumptions, reproducing key steps, and capturing the right method alongside the wrong one.
Bring scientific and technical domain expertise into AI reasoning, research, and dataset review.

Responsibilities

• Review AI outputs against current physics methods, conventions, and prior work for Physical Sciences Research Assistant assignments.
• Reproduce or sanity-check key derivations, calculations, or experimental claims.
• Flag dimensional, methodological, and citation errors with structured severity tags.
• Capture the corrected reasoning or worked example so the modeling team can train on it.

Role details

Track STEM research review Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Graduate-level training or equivalent applied experience in physics or a closely related field for Physical Sciences Research Assistant work.
• Hands‑on experience publishing, teaching, or advising on the topic at a professional level.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that cites methods, papers, or worked examples.
• Reliable async availability for at least 10 hours per week.

Role signals

Example tasks

• Reproduce a physics derivation from a model output and flag any algebraic or dimensional errors.
• Grade a model's literature summary against the cited papers and rate the citation quality.
• Adjudicate a disputed answer between two reviewers using textbook methods.
• Audit a 25-row batch for rubric consistency and report drift to the program lead.

Useful experience

• PhD, postdoc, or industry research experience in the topic area.
• Prior work reviewing AI-assisted research tooling and its failure modes.
• Multilingual fluency for non-English papers and corpora.

Compensation and schedule

Hourly rate confirmed after the interview process.
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Scientific reasoning
• Method validation
• Citation review
• Quantitative analysis
• Physics

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.

• 01 Confirm profile and eligibility details.
• 02 Attach this role deliberately.
• 03 Receive a human review decision or follow‑up.

Placement timing depends on program demand and reviewer confirmation.