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Point72

Quantitative Developer Intern

New YorkinternshipinternAdded today

About this role

Point72's Internal Alpha Capture team seeks quantitative developer interns to build and optimize production trading infrastructure using Java, machine learning signals, and real-time market data systems. You'll work on low-latency components, ETL pipelines, and testing frameworks that directly support systematic equity trading strategies.

What you'll do

  • Develop low-latency Java components including messaging handlers and market data parsers for equities trading platform
  • Design and implement features to support trade execution workflows and convert research signals into production logic
  • Optimize high-throughput data ETL pipelines, caches, and SQL databases for real-time market data processing
  • Write robust unit and integration tests with automated testing to ensure system correctness under live market conditions
  • Improve monitoring, logging, alerting, and trading infrastructure tools
  • Take ownership of projects from design through deployment, working independently and collaboratively

What they're looking for

  • Java programming with production-level coding quality
  • Object-oriented design and architecture
  • Docker containerization and orchestration
  • Unix/Linux environments and shell scripting
  • Git version control and CI/CD tools
  • SQL and database optimization
  • Software testing (unit and integration)
  • Machine learning or quantitative domain knowledge
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Point72

Point72 operates trading, data infrastructure, and investment management technology platforms that power systematic portfolio management and capital markets operations. The company is hiring infrastructure engineers, data reliability specialists, and software engineers to build and maintain mission-critical systems including data pipelines, network architectures, storage platforms, and research tools.

Website
point72.com
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Likely interview questions

  • Describe a complex Java system you built from scratch—how did you ensure it was production-ready and maintainable?
  • Walk us through how you've optimized a high-throughput data pipeline; what bottlenecks did you identify and how did you resolve them?
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