Remote Data Analytics SME for Frontier AI Tasks (20h/wk)

Halluminate, Inc.; United States; Remote; USD 138,000 - 276,000; Part time

Benefits offered by this job

Training milestone payments

Bonuses for above-average performance

Fully remote, flexible schedule

Job summary

Halluminate is hiring part-time Data Analytics Subject Matter Experts to design and develop complex RL tasks for frontier AI models. You will craft multi-step problems across financial services workflows, providing data-driven insights to improve training data and frontier evaluations.

Your day-to-day involves creating challenging tasks, evaluating model outputs with researchers, and iterating based on reviewer feedback while maintaining high-quality standards.

Qualifications

• 1.5+ years of experience in data analytics
• Experience with dashboards and data storytelling
• Ability to work independently with minimal supervision

Responsibilities

• Shape the future of knowledge work by identifying which financial workflows matter most and where today’s AI models fall short, drawing on your real experience
• Build training materials for AI systems, including cohort and retention analyses, KPI dashboards, A/B test readouts, and other real-world deliverables
• Create challenging tasks that expose weaknesses in state-of-the-art AI models on financial services work
• Contribute to frontier evaluations that measure how leading AI models perform on real finance workflows
• Work directly with research and engineering teams at frontier labs to evaluate model outputs and turn findings into better training data
• Revise tasks quickly based on feedback from reviewers and model results, often across multiple rounds

Skills

Data analysis

Attention to detail

Time management

Remote collaboration

Tools

SQL

Excel

PowerPoint

Job description

Halluminate is hiring part-time Data Analytics Subject Matter Experts to design and develop complex RL tasks for frontier AI models. You will craft multi-step problems across financial services workflows, providing data-driven insights to improve training data and frontier evaluations.

Your day-to-day involves creating challenging tasks, evaluating model outputs with researchers, and iterating based on reviewer feedback while maintaining high-quality standards.