Finance and Accounting Professional for AI Reinforcement Learning Study
Job Description
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What We're Researching
We're running a paid study on how financial data and workflows can be accurately modeled inside reinforcement learning environments. By building realistic accounting scenarios, we can better train artificial intelligence systems to handle complex financial decision-making. Your domain expertise will directly inform the structure and logic of these simulated worlds.
How It Works
During this remote session, you will interact with our reinforcement learning platform tailored for financial workflows. You will be asked to map out common finance and accounting scenarios, detailing the variables, constraints, and potential outcomes. The platform will guide you through building these simulated worlds while you explain your logical choices. You will also provide feedback on the intuitive feel and accuracy of the modeling tools provided.
Who This Is For
We are looking for experienced finance and accounting professionals with a strong grasp of complex financial modeling. We welcome certified public accountants, financial analysts, quantitative analysts, and corporate finance managers. A technical background or prior experience with artificial intelligence platforms is helpful but not strictly required.
What You'll Do
- Interact with a reinforcement learning platform designed for the financial sector
- Map out realistic finance and accounting scenarios using platform tools
- Define variables, constraints, and logic for complex financial workflows
- Provide feedback on the accuracy and usability of the scenario builder
Who Should Apply
- Active professional in finance, accounting, or quantitative analysis
- Strong understanding of complex financial workflows and decision logic
- Comfortable interacting with new technical platforms and modeling tools
- Ability to clearly articulate accounting principles and scenario constraints
Compensation
$90 per hour
About Terac
Terac is building the world's largest pool of vetted human experts for AI. Researchers, AI labs, and product teams use Terac to recruit, screen, and pay study participants across industries, languages, and skill sets.
Learn more at terac.com or on YouTube at @jointerac.
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Terac
View Company ProfileTerac is an elite, enterprise-grade AI-native market research and expert data orchestration platform engineered to operate as the definitive, high-velocity infrastructure layer connecting frontier AI models and enterprise product teams with on-demand human expertise. Operating as a mission-critical "intelligence infrastructure layer" for the modern data economy, the company eliminates the severe operational friction of traditional qualitative research—which frequently suffers from slow participant sourcing, manual scheduling bottlenecks, and multi-week analysis cycles—by deploying automated, voice-based AI agents capable of conducting natural, personalized interviews with hundreds of participants simultaneously. Moving far beyond rigid legacy survey tools, Terac serves a dual purpose: it acts as a scalable consumer insights engine for product teams and a highly vetted marketplace for the human data economy, allowing frontier AI labs and research institutions to instantly tap into specialized, high-stakes human judgment for model evaluation and RLHF. Under the hood, their sophisticated proprietary validation infrastructure—backed by top-tier venture capital—natively handles automated participant recruitment, strict quality-control vetting, smart routing, multi-region payouts, and immediate programmatic synthesis of unstructured conversation into structured, actionable data graphs. What sets Terac apart is its uncompromising dedication to shifting how humans and AI collaborate at scale; by bridging the gap between performance-intensive, data-hungry enterprise workflows and accessible, flexible expert panels, the firm enables global technology organizations to radically accelerate their product-market loops, eliminate data engineering bottlenecks, and build an unassailable foundation for continuous model and product alignment.
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