Finance Expert — Quant Research & Systematic Trading

About this role

**About the Opportunity** A leading AI research organization is seeking experienced finance professionals to help evaluate how advanced AI systems perform in quantitative research, systematic trading, backtesting, and portfolio construction. This is an expert-evaluation engagement. Rather than joining a traditional finance team, you will apply your professional judgment to assess domain-specific tasks, the environments in which those tasks are completed, and the rubrics used to evaluate performance. The exact task content and research workflows are confidential and may vary over the course of the project. **What You'll Do** - Evaluate whether finance tasks are realistic, well-scoped, internally consistent, and representative of professional practice - Inspect task environments, provided information, and expected workflows for clarity, usability, and domain fidelity - Review scoring rubrics for technical correctness, completeness, objectivity, and alignment with the stated task - Identify ambiguity, missing assumptions, unsupported requirements, edge cases, and criteria that could reward incorrect work - Apply rubrics to sample outputs and assess whether they distinguish strong work from superficially plausible or materially flawed work - Provide precise, structured written feedback and recommend targeted improvements - Participate in calibration and incorporate reviewer feedback to support consistent evaluation standards **Who We're Looking For** - 5–8 years of relevant experience for the researcher track, or 8–15 years for the senior reviewer track - Professional experience in quantitative research, systematic trading, portfolio construction, or a closely related field - Strong Python skills and hands-on experience building or reviewing quantitative research - Deep understanding of research bias, data quality, validation, transaction costs, risk, and implementation constraints - Excellent written communication and the ability to explain not only whether something is correct, but why - High attention to detail, sound professional judgment, and comfort evaluating work against explicit standards - Experience reviewing analysts, approving work, designing controls, or setting quality standards is especially valuable for the senior track **Why This Work Matters** Advanced AI systems need to be evaluated against standards that reflect genuine professional practice, not just plausible-looking answers. Your expertise will help ensure the tasks, environments, and rubrics used in this research measure meaningful finance capability with rigor and consistency.

Skills & domains

• ai-training
• rlhf
• sme
• annotation
• Finance