$105 / hr

Hourly contract

Remote

To improve the quality, usefulness, and reliability of general-purpose conversational AI systems. These systems are used across a wide range of everyday and professional scenarios, and their effectiveness depends on how clearly, accurately, and helpfully they respond to real user questions.

In financial contexts, even small errors or unclear reasoning can have significant downstream impact. This project focuses on evaluating and improving how conversational AI systems reason about, explain, and respond to finance-related queries. Your expertise helps ensure model outputs reflect real-world financial knowledge, sound quantitative reasoning, and clear professional communication.

What You’ll Do

Write and refine prompts to guide model behavior in financial contexts

Evaluate LLM-generated responses to finance-related user queries for accuracy, reasoning quality, and clarity

Conduct fact-checking using trusted public sources, financial references, and external tools

Annotate model responses by identifying strengths, areas of improvement, and factual or conceptual inaccuracies

Assess tone, completeness, and appropriateness of responses for real-world financial use cases

Ensure model responses align with expected conversational behavior and system guidelines

Apply consistent evaluation standards by following clear taxonomies, benchmarks, and detailed evaluation guidelines

Who You Are

You have a minimum of 5 years of real-world professional experience in Finance, supported by an associated Bachelor’s, Master’s, or PhD degree (e.g., Finance, Accounting, Economics, Business, or related fields)

You have experience in one or more of the following sub-domains:

Investment Banking

Corporate Finance

Accounting & Auditing

Asset Management

You have significant experience using large language models (LLMs) and understand how and why people use them

You have excellent writing skills and can clearly explain complex financial concepts

You have strong attention to detail and consistently notice subtle issues others may overlook

Nice-to-Have Specialties

Prior experience with RLHF, model evaluation, or data annotation work

Experience writing or editing high-quality financial written content

Experience translating complex financial ideas for non-expert audiences

Familiarity with evaluation rubrics, benchmarks, or quality scoring systems

What Success Looks Like

You identify financial inaccuracies, faulty assumptions, and weak reasoning in model responses

Your feedback directly improves the accuracy and usefulness of finance-related AI outputs

Apply Here

You deliver clear, reproducible evaluation artifacts that customers can act on

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