AI Trainer - Finance

About The Role is looking for experienced finance professionals to evaluate how frontier AI models handle real finance work: valuation and modeling, credit analysis, investment memos, budgeting and variance analysis, treasury and risk decisions. You bring the judgment you have built sending analyst notes back for buried assumptions, defending a model to an investment committee, or explaining a variance to a CFO. We bring the model output that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a three statement or DCF model with a valuation summary, a credit memo and covenant analysis, an investment committee memo, a budget versus actual variance analysis, a hedging or liquidity proposal, or a portfolio attribution, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a practitioner in your field actually handles, which you assemble yourself. Some tasks are compact, built around a handful of files; others are larger scenarios that take several days to build. In every case the goal is the same: a task a competent professional in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional finance role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic finance tasks drawn from your own day to day work: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files a professional would need (financial statements, models, data extracts, term sheets, board or IC materials, correspondence), which you author yourself. Run those tasks through frontier AI models and evaluate the deliverable they produce (the model, memo, analysis or deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right assumptions stated, the right calculations, the right risks flagged and the right recommendation, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: broken model logic, unsupported assumptions, misread financials, fabricated or ignored source files, unit and scaling errors, missed risks, and off brief interpretation of the ask. Contribute across valuation and modeling, credit, FP&A, markets, treasury and risk, and review and refine tasks built by other experts. Domain Qualifications 2+ years of hands on experience preferred in corporate or investment finance. In progress Bachelor’s degree or higher in finance, economics, business or a quantitative field. CFA, FRM or securities licenses welcome but not required. Depth in at least one of: equity or fixed income research; portfolio management or trading; corporate finance and FP&A; credit analysis and underwriting; investment banking, M&A or corporate development; treasury and liquidity; market, credit or liquidity risk; wealth management; insurance, actuarial or underwriting. Working understanding of several of the others, enough to know what those workflows involve and how they are run, so you can assess work in an adjacent area and point out what was done correctly or incorrectly. Builds and audits models at a practitioner level: three statement, DCF, comps, credit or budget models, not only consumes their outputs. CFA, CPA, FRM or securities licenses are welcome but not required and not screened for. Practical expertise outweighs credentials. General requirements 2+ years of hands on experience in your field preferred (see Domain qualifications above). Candidates with less experience are considered where the practical work is real. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex professional reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the subject sits partly outside your own specialization. You verify what a document claims against the underlying numbers, sources or facts. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and full time hours are available. Based in the United States, Canada, or the UK.