InfiniteQuant

InfiniteQuant

Quantitative Researcher - Internship - Summer 2027

New York, NY, United States · Intern · Internship

Sponsorship not specifiedDetected 53 days ago
PythonMachine LearningDeep LearningNumPyStatisticsResearchMentoring

About the role

  • Company Description InfiniteQuant is a global quantitative trading and technology company.
  • As a privately owned and funded proprietary trading firm, we focus on high-frequency quantitative trading across global financial markets.
  • Everything from data to strategy, simulation, and trading systems is developed in-house.

Responsibilities

  • We seek to recruit, develop, and retain the most talented and qualified applicants from a diverse candidate pool.

Requirements

  • Experience in leading HFT prop shops, trading firms, or hedge funds.
  • Proficiency in data-driven research, advanced statistics, and strategy development is expected.
  • Proficiency in C++.

Nice to have

  • Work or internship experience in crypto trading is a plus.
  • competitive experience on Kaggle or similar platforms is a big plus

Compensation

  • $6,000-$10,000 per month

Benefits

  • Earn performance-based bonus.
  • Machine Learning / Deep Learning experience

Company info

  • Company Description
  • InfiniteQuant is a global quantitative trading and technology company.
  • Analyze order book data and market trade data to generate high-frequency signals with strong statistical significance.
  • Directly responsible for the construction of alpha signals or monetization for latency-sensitive, capacity-constrained strategies.
  • Engage in sports and prediction market trading using quantitative pricing and liquidity management techniques.
  • Monitor, track, and analyze sports prediction markets, including betting odds, price movements, and market sentiment, and provide insights for predicting sports outcomes.

Visa & Work Authorization

  • USA or UAE working visa sponsorship for qualified candidates if needed

This listing is sourced directly from InfiniteQuant's careers page and normalized into a canonical job model.