
Finance Research Technical Staff - Remote
Listing checked September 12, 2026 · pay as published by Micro1
Overview
This full-time remote position sits at the intersection of large language models, agentic systems, and enterprise finance. You will join a research team to build evaluation frameworks that measure and advance AI performance on complex financial workflows. The role involves applied research, developing methodologies for assessing financial intelligence in AI, and working with researchers, engineers, and product teams. Compensation is $6-$8 per hour.
What You'll Do9
- 1Create and maintain evaluation frameworks for AI agents in finance, including benchmark suites, scoring methods, and quality rubrics.
- 2Conduct original research on financial reasoning and decision-making, turning findings into measurable AI improvements.
- 3Build and curate datasets, test cases, and benchmark environments that reflect real-world enterprise finance challenges.
- 4Work with AI researchers, ML engineers, and product teams to evaluate and enhance finance-focused models and agentic systems.
- 5Analyze model behavior, failure modes, and performance trends to derive insights and set research priorities.
- 6Produce internal research reports, technical documentation, and best practices for AI in finance.
- 7Keep up with advances in AI, machine learning, financial modeling, and evaluation science, integrating new techniques into projects.
- 8Help set research standards and thought leadership for enterprise financial intelligence and AI evaluation.
- 9Promote scientific rigor, experimentation, and collaboration in a distributed research setting.
Requirements7
- 1Advanced degree in Finance, Economics, Financial Engineering, Quantitative Finance, or a related field. PhD preferred; MBA, CFA, or equivalent experience also considered.
- 2Deep expertise in at least one finance area: investment research, capital markets, risk management, corporate finance, accounting, or financial analysis.
- 3Proven experience with rigorous research, analytical methodology development, or evaluation frameworks in finance or quantitative fields.
- 4Ability to translate ambiguous financial problems into measurable research questions and evaluation criteria.
- 5Strong analytical and critical-thinking skills, with a history of solving complex, multidisciplinary problems.
- 6Excellent written and verbal communication, including experience with research reports, technical documentation, or thought leadership.
- 7Experience in collaborative, cross-functional environments with research, engineering, and product stakeholders.
Who Should Apply
This role suits candidates with an advanced degree in finance or a quantitative field and deep domain knowledge in areas like investment research or risk management. You should have a track record of conducting rigorous research and building evaluation frameworks. The position is less ideal for those without a strong finance background or experience in AI evaluation. A common reason candidates get rejected is lacking demonstrated research experience in finance or AI. Another is insufficient ability to translate ambiguous financial problems into measurable research questions.
Salary Insight
The pay rate for this position is $6 to $8 per hour. No other compensation details are provided in the source.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$78/hour
This role
$6–$8/hr
Most Machine Learning & AI roles pay $55–$105 per hour. This role's pay sits below that range.
Based on 552 similar roles that publish pay · 94 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
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Hiring most right now: micro1 (262) · Mercor (95) · SME Careers (63)
Most requested skills · share of roles
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- technical writing8%
- llm evaluation8%
- ai evaluation7%
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Compensation
$6–8/hr
Required Skills
Application Tip
Highlight your experience with AI evaluation frameworks and financial reasoning. Provide specific examples of benchmarks or methodologies you have developed, and quantify improvements in AI performance where possible.
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