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IMC

Performance Engineer Intern - Summer 2027

Chicago, United StatesFrom $200kinternshipinternAdded today

About this role

IMC Trading seeks a driven intern to apply quantitative research and experimentation to optimize ultra-low-latency trading strategies and exchange technology. You'll develop Python-based AI agents and algorithms while collaborating with traders and engineering teams to enhance liquidity strategies and understand market microstructure.

What you'll do

  • Conduct quantitative research on large datasets to analyze exchange technology and microstructure
  • Design optimization strategies for liquidity taking and quoting across different exchanges
  • Build AI agents, ETL pipelines, and algorithms in Python for ultra-low-latency trading systems
  • Reverse-engineer and stress-test network and systems programming technologies
  • Collaborate with traders, hardware engineers, and software engineers to implement research findings

What they're looking for

  • Proficiency in Python or other programming languages
  • Strong quantitative and analytical abilities
  • Statistical modeling and machine learning
  • Network technology and systems programming knowledge
  • Exchange microstructure understanding
  • Communication and cross-functional collaboration
  • Problem-solving and competitive drive
  • Interest in financial markets

Benefits

  • Discretionary bonus eligibility
  • Paid leave
  • Insurance coverage
  • Exposure to cutting-edge trading technology
  • Mentorship from world-class researchers and engineers
  • Collaborative, high-performance culture
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IMC

IMC builds trading technology and financial systems powered by software, machine learning, and hardware engineering. The company is hiring interns and graduate-level engineers and researchers across software, machine learning, and hardware disciplines to develop trading algorithms, research strategies, and collaborative technology solutions.

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Likely interview questions

  • Walk us through a time you optimized performance in a system—what metrics did you focus on and how did you measure improvement?
  • Describe your experience with Python and any large-scale data processing projects you've built.
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