Financial Professional

Uber AI Solutions; United States; Remote; USD 96,000 - 165,000; Part time

Job summary

Uber AI Solutions is seeking a Finance Domain Expert (SME) to design and implement evaluation frameworks for financial GenAI applications. Remote US opportunity (excluding California) with freelance/part-time engagement.

You’ll bridge corporate finance expertise with advanced LLM behavior, build multi-dimensional rubrics, curate datasets, and train annotators globally to ensure high-quality model alignment.

Qualifications

• 5+ years in Corporate Finance, Investment Banking, Equity Research or Quantitative Finance.
• Proven experience with RLHF pipelines, data labeling platforms, prompt engineering or LLM evaluations.
• Demonstrated success building qualitative evaluation rubrics, QA frameworks or scoring methodologies from scratch.

Responsibilities

• Rubric Architecture & Framework Design from scratch to measure LLM performance in finance.
• Dataset Curation & Prompt Engineering for synthetic financial data and edge-case prompts.
• RLHF & Fine-Tuning: provide expert ratings, edits and reasoning justifications.
• Capability Building & Scale: training materials and calibration for globally distributed annotators.

Skills

Corporate Finance

Investment Banking

Equity Research

Quantitative Finance

GenAI / LLM Exposure

RLHF Pipelines

Prompt Engineering

Evaluation Rubrics

QA Frameworks

Training & Calibration

Job description

Finance Domain Expert – GenAI & LLM Evaluation (SME)

Location: Remote US (Excluding California)

Engagement Type: Freelance / Part Time

Role Overview

At Uber AI Solutions, we partner with the world’s leading enterprises and AI research labs to train and evaluate frontier-level Generative AI models.

As a Finance Domain Expert (SME), you will serve as the architect of quality and evaluation frameworks for financial GenAI applications. You will bridge the gap between high-level finance (corporate finance, investment banking, accounting, and quantitative analysis) and advanced LLM behavior.

Your core mission is to build evaluation capabilities from the ground up: defining strict, multi-dimensional rubrics, authoring complex reasoning datasets, and training a global cohort of annotators to evaluate financial AI outputs.

Key Responsibilities

1. Rubric Architecture & Framework Design (From Scratch)

• Define the Standard: Design and document end-to-end evaluation rubrics, taxonomies, and style guides to measure LLM accuracy, reasoning, compliance, and hallucination rates in financial contexts.
• Define Financial Reasoning: Establish granular evaluation dimensions tailored to finance (e.g., distinguishing between GAAP vs. IFRS standards, validating financial statement linkage, identifying subtle calculation errors in discounted cash flow models).
• Calibrate Quality: Build "gold standard" comparison benchmarks (side-by-side criteria, rank-ordering scales, and rating frameworks) that ensure consistent scoring across diverse global teams.

2. Dataset Curation & Prompt Engineering

• Complex Data Generation: Create high-quality, synthetic financial prompt-and-response datasets (synthetic data creation) representing real-world user behaviors.
• Edge-Case Simulation: Design adversarial prompts to stress-test financial LLMs on regulatory compliance, risk modeling, tax code nuances, and strategic investment analysis.
• RLHF & Fine-Tuning: Provide expert-level preference ratings, edits, and reasoning justifications to drive Reinforcement Learning from Human Feedback (RLHF) pipelines.

3. Capability Building & Scale

• Empower the Workforce: Design and deliver training materials, masterclasses, and calibration bootcamps for globally dispersed annotator cohorts via our eLearning platforms.
• Quality Assurance (QA) Operations: Perform second-tier spot checks and consensus resolution on labeling queues, serving as the ultimate arbiter for complex financial disputes.
• Feedback Loops: Partner with Uber AI Solutions product teams and client engineers to turn metric trends into actionable model-alignment strategies.

Required Qualifications

Professional Experience

• Domain Depth: 5+ years of professional experience in Corporate Finance, Investment Banking, Equity Research, Quantitative Finance
• GenAI / LLM Exposure: Proven experience working on RLHF pipelines, data labeling platforms, prompt engineering, or LLM system evaluations.
• Methodology Expert: Demonstrated success building qualitative evaluation rubrics, QA frameworks, or scoring methodologies from scratch in corporate or academic settings.