About this role
Job Description
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.
What you'll do
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Interact with AI models on probability, statistics, quantitative modeling, time-series analysis, and research programming tasks.
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Create challenging quantitative problems, reference solutions, and reproducible worked examples.
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Check AI-generated mathematics and code, test assumptions, and identify data leakage, overfitting, or flawed backtesting logic.
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Compare and annotate model responses, explain errors clearly, and document the reasoning behind a high-quality solution.
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What you'll learn
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Learn how domain-specific examples and human feedback are used to support AI post-training.
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Gain practical experience evaluating AI responses, applying annotation guidelines, and checking dataset quality.
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See how factual accuracy, clear reasoning, and consistent feedback help teams assess and improve AI model behavior.
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Build experience at the intersection of your academic field and applied AI through project work and feedback.
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Work arrangement
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Remote internship
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Collaborate with the team on scoped AI data and evaluation projects
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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.
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Why Apply
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Practical experienceApply your coursework to real project briefs and build work you can explain with confidence.
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Learn through feedbackDevelop your judgment by documenting your approach and incorporating team feedback.
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Meaningful AI workHelp evaluate and improve AI outputs in financial & professional.
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Remote collaborationWork with the team online and agree on your project scope and schedule before starting.
Responsibilities
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Work with the team to turn a scoped project brief into a clear, documented deliverable.
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Validate source data, calculations, and assumptions before sharing results.
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Review AI-generated outputs against reference material and document discrepancies.
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Share progress, incorporate feedback, and explain findings clearly.
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Required Qualifications
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Currently enrolled in a master’s or higher degree program in mathematics, statistics, computer science, quantitative finance, or a related field.
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Foundational knowledge of probability, statistics, and linear algebra.
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Comfort with Python and data-analysis libraries, or a comparable research programming language.
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Ability to explain assumptions, reproduce calculations, and assess limitations honestly.
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Company Description
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Alpheva AI builds the expert data, evaluations, and real-world environments that help frontier AI systems become more capable and reliable. We partner with experienced professionals whose judgment can teach models how high-quality work is actually done.