Quant Intern

Work directly with AI systems to create high-quality quantitative data and help improve their mathematical and research reasoning. Apply your graduate-level knowledge of probability, statistics, and programming while learning how expert-created data supports AI post-training and evaluation.

• Interact with AI models on probability, statistics, quantitative modeling, time-series analysis, and research programming tasks.
• Create challenging quantitative problems, reference solutions, and reproducible worked examples.
• Check AI-generated mathematics and code, test assumptions, and identify data leakage, overfitting, or flawed backtesting logic.
• Compare and annotate model responses, explain errors clearly, and document the reasoning behind a high-quality solution.

• Learn how domain-specific examples and human feedback are used to support AI post-training.
• Gain practical experience evaluating AI responses, applying annotation guidelines, and checking dataset quality.
• See how factual accuracy, clear reasoning, and consistent feedback help teams assess and improve AI model behavior.
• Build experience at the intersection of your academic field and applied AI through project work and feedback.

• Remote internship
• Collaborate with the team on scoped AI data and evaluation projects

Project scope, schedule, tools, and expectations will be discussed during the application process. Use only approved materials; do not share confidential employer, client, or university research data.

• Currently enrolled in a master's or higher degree program in mathematics, statistics, computer science, quantitative finance, or a related field.
• Foundational knowledge of probability, statistics, and linear algebra.
• Comfort with Python and data-analysis libraries, or a comparable research programming language.
• Ability to explain assumptions, reproduce calculations, and assess limitations honestly.

Alpheva is a new expert network (launched September 2026); payout methods are not published yet, so confirm your first payment before scaling up.