LLM Research Scientist (Pre-training & Post-Training)

AI Trainer Jobs; United States; Remote; USD 138,000 - 165,000; Part time

Job summary

AI Trainer Jobs seeks an LLM Research Scientist (Pre-training & Post-Training) for a remote review track evaluating AI outputs across research reasoning, calculations, and workflows. Reviewers grade derivations and reproduce key results, documenting the correct method so the modeling team can train on it.

As a contractor, you'll work remotely with compensation of $100–$120 per hour for US-eligible work, with flexible scheduling and async collaboration expectations.

Qualifications

• Graduate-level training or equivalent applied experience in research science.
• Hands-on publishing, teaching, or advising on the topic 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 research science methods and prior work.
• Reproduce or sanity-check key derivations, calculations, or experimental claims.
• Flag dimensional, methodological, and citation errors with structured severity tags.
• Capture corrected reasoning or worked examples for training data.

Skills

Graduate-level training

Experience publishing

Rubric application

Clear written reasoning

Async availability

Education

PhD/postdoc/industry research

Job description

LLM Research Scientist (Pre-training & Post-Training) is a remote review track for evaluating AI outputs across research science 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: $100–$120 / hr · Location: Remote — US-eligible · Contractor

LLM Research Scientist (Pre-training & Post-Training) is a remote review track for evaluating AI outputs across research science reasoning, calculations, and research workflows.

About the role

LLM Research Scientist (Pre-training & Post-Training) is a remote review track for evaluating AI outputs across research science 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.
Research science 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 research science methods, conventions, and prior work for LLM Research Scientist (Pre-training & Post-Training) 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 $100–$120 / hr Eligible from US

What you should bring

• Graduate-level training or equivalent applied experience in research science or a closely related field for LLM Research Scientist (Pre-training & Post-Training) 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.

Example tasks

• Reproduce a research science 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

$100–$120 / hr
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
• Research science

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.