Real Assets & Project Finance Workflow Reviewer

About OpenTrain

OpenTrain is the #1 platform for building careers in AI training and data labeling. We help experienced practitioners find focused, high-impact projects where their domain expertise directly improves frontier AI systems. Creating an OpenTrain account is free, and OpenTrain AI is the hiring and contracting organization for this role.

About AI training and why task realism matters

AI training (aka data labeling or human feedback) is the human work that teaches models how to behave: annotating, evaluating, and reviewing examples so AI systems learn useful, professional-grade skills. For finance use cases, realistic tasks and accurate evaluations are essential so models learn to reason about cash flows, structuring, and risks the same way practitioners do.

The role

We are recruiting reviewers for a high-engagement Finance Sprint focused on real assets and project finance workflow realism. You will assess whether annotated tasks and evaluation prompts reflect authentic professional practice and provide structured recommendations to raise the bar on quality and realism.

• Commitment: 20+ hours per week (part-time contract, short-term sprint)
• Pay: USD $150–$200 per hour (PAY_PER_HOUR)
• Work type: Remote / worldwide (English required)
• Data: Text-based evaluation tasks; label type: evaluation_rating
• Employment: Contractor, part-time

What you'll do

Your core mission is to judge whether finance tasks, inputs, and expected workflows mirror how work is actually done in real-estate underwriting, project finance, infrastructure, energy, mining, and natural-resources investing, then provide concise, actionable feedback to improve task design and evaluation.

• Review tasks and workflows for professional realism, scope, clarity, and internal consistency
• Assess whether inputs, context, and required steps match real-world underwriting and project finance practice
• Identify missing assumptions, ambiguous instructions, impractical constraints, and material edge cases
• Flag tasks that are too broad, generic, or unlikely to measure real finance capability
• Deliver concise, structured written feedback and targeted recommendations to improve realism
• When needed, cross-check task logic with trusted domain peers to maintain a high bar across the sprint

Requirements

This is an expert-level reviewer role. Candidates must have deep, practical finance experience and strong communication skills to explain whether something is correct and why.

• Experience: 5–10 years for practitioner track or 10–16 years for senior reviewer track in real-estate underwriting, project finance, infrastructure, energy, mining, or natural-resources investing
• Technical skills: strong cash-flow modeling, valuation, debt-structuring, and sensitivity-analysis skills
• Judgment: ability to connect legal, technical, operating, and market inputs to financial outcomes
• Quality: high attention to detail and comfort evaluating work against explicit standards
• Leadership (especially for senior track): experience reviewing analysts, approving work, designing controls, or setting quality standards
• Communication: excellent written communication and ability to explain not just correctness but rationale

Helpful background

Familiarity with AI evaluation principles and the role of task realism in training data is a plus, as is prior experience giving structured feedback in fast-paced projects. You do not need prior labeling experience if you can reliably assess professional workflow realism and write clear recommendations.

Who should apply and next steps

Apply if you are an experienced real assets or project finance professional who wants to help shape how AI systems learn core finance workflows. This sprint is a short-term, high-impact engagement where your practical expertise will directly improve evaluation quality and model reliability.

To apply, create an OpenTrain account, complete your profile, and submit your application listing relevant experience and examples of modeling or review work. Selected reviewers will be contacted with sprint details, onboarding materials, and target timelines.

• Ideal for practitioners and senior reviewers with 5–16 years of hands-on finance experience
• Short-term sprint: flexible hours but a 20+ hour weekly commitment expected
• OpenTrain provides the contracting relationship and consolidates your experience into a professional AI training portfolio