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Research EngineerAquatic Capital ManagementNew York, New York, United States
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Research Engineer

Aquatic Capital Management
  • US
    New York, New York, United States
  • US
    New York, New York, United States
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Über

Aquatic Capital Management is seeking a Research Engineer
to join our research team. Your work will blend software engineering and quantitative research, focusing on designing high-performance tooling and infrastructure that accelerate the development of machine learning models used in systematic trading.

You will work closely with quantitative researchers to build the next generation of modeling platforms —from applying numerical computation methods, to training deep learning models. The software you develop will play a central role in driving the firm's machine learning strategy.

Role Details:

  • Scale neural network training systems to support larger, more complex models while maintaining the ability to run many experiments at once.
  • Explore and prototype novel modeling approaches (e.g., more efficient decision trees, the latest transformer variant).
  • Collaborate with researchers to take promising models quickly and reliably into production.
  • Profile and optimize distributed model training and inference–for example, by using GPU acceleration and the latest ML compiler technologies.
  • Establish best practices for a clean, reproducible, and maintainable research codebase where new ideas can be quickly validated.

Technical Experience:

  • Strong software engineering skills with experience in machine learning, numerical computing, and/or applied ML research
  • Proficiency with Python and numerical + ML frameworks (PyTorch, JAX, numpy), familiarity with PyTorch 2.x compiler stack (e.g., TorchScript, TorchInductor) is a plus
  • Practical experience deploying ML models or infrastructure in a research and production environment
  • BS/MS/PhD in Computer Science, Applied Math, Engineering, or a related technical field
  • New York, New York, United States

Sprachkenntnisse

  • English
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