Education & Learning EvaluationRecord 001
Machine Learning Researcher (MSc. or PhD) — Remote AI Evaluation
Machine Learning Researcher (MSc.
ContractorRemote · Independent specialist contractorHourly rate confirmed after the interview process.
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Human Data / Education & Learning Evaluation
Machine Learning Applied Scientist — AI Evaluation (MSc or PhD)
Machine Learning Applied Scientist — AI Evaluation (MSc or PhD) is a remote review track for evaluating AI outputs across applied scientists machine learning specialist operations workflows.
ContractorRemote — US-eligibleHourly rate confirmed after the interview process.
About the role
Machine Learning Applied Scientist — AI Evaluation (MSc or PhD) is a remote review track for evaluating AI outputs across applied scientists machine learning specialist operations workflows. Reviewers grade workflow correctness, policy adherence, and stakeholder fit; flag operational risk; and document the right next step so the modeling team can train on it.
Applied Scientists Machine Learning specialist operations AI has to fit into an actual day at work. AuraOne uses experienced operators to grade outputs the way a senior peer would — checking workflow, policy, and the unwritten rules that decide whether a task actually gets done.
Review tutoring and assessment output. Judge it against how real learners use it.
Responsibilities
- Review AI outputs against current applied scientists machine learning specialist operations workflows, playbooks, and firm policy for Machine Learning Applied Scientist — AI Evaluation (MSc or PhD) assignments.
- Grade tone, escalation logic, and stakeholder fit on a structured rubric.
- Flag operational risk, missed escalations, and policy-adherence gaps with severity tags.
- Capture the right next step so the modeling team can train on it.
- Adjudicate disputed treatments against published playbooks or firm guidance.
- Maintain reviewer-quality scores in inter-rater calibration cycles.
What you should bring
- Direct working experience in applied scientists machine learning specialist operations on real teams for Machine Learning Applied Scientist — AI Evaluation (MSc or PhD) work.
- Comfort applying multi-page rubrics consistently across long batches.
- Clear written reasoning that names the policy or workflow being applied.
- Strong attention to detail and the ability to flag when a prompt itself is the problem.
- Reliable async availability for at least 10 hours per week.
Role signals
Example tasks
- Grade a model's response to a real applied scientists machine learning specialist operations ticket and rate workflow, tone, and escalation.
- Flag a missed escalation with the right severity tag and corrected next step.
- Adjudicate a disputed playbook call between two reviewers using firm guidance.
- Audit a 25-row batch for rubric consistency and report drift to the program lead.
Useful experience
- Prior experience training, calibrating, or QA-ing operations teams.
- Familiarity with AI-assisted workflow tooling and its failure modes.
- Bilingual experience for cross-region operations.
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
- Operational review
- Policy adherence
- Workflow judgment
- Stakeholder communication
- Applied Scientists Machine Learning specialist operations
Application process
Submitting this form sends your application directly to the recruiting team for this specialist cohort.