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2 дня назад

Quantitative Developer (Python)

Формат работы
onsite
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Quantitative Developer (Python) (Core Data Sets): Building and scaling data flows and packages that support signal generation for quantitative researchers and strategists with an accent on performance, robustness, and large-scale data processing. Focus on optimizing Python tooling, benchmarking systems, and solving complex challenges involving Linux, memory, I/O, networking, and data structures.

Location: Onsite in Bala Cynwyd, Philadelphia Area, Pennsylvania, United States

Company

hirify.global is a global quantitative trading firm focused on derivatives and systematic trading across listed financial products and asset classes.

What you will do

  • Design, develop, and scale the Quant Core Data framework for efficiency and robustness.
  • Build packages used by quantitative researchers and strategists to advance signal generation technology.
  • Optimize existing tooling through greenfield development and performance-focused engineering.
  • Work with large datasets while maintaining data cleanliness and correctness.

Requirements

  • At least 3 years of experience developing software in Python or C++ under strict performance requirements.
  • Experience with performance analysis, benchmarking, large-scale computing, and performance trade-offs.
  • Strong systems fundamentals, including virtual memory, NUMA, I/O, and networking.
  • Deep knowledge of Linux, Python data analysis or low-level Python, data structures, and algorithms.
  • Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related discipline; an advanced degree is preferred.

Culture & Benefits

  • Immediate start opportunity.
  • Intellectually driven and highly collaborative environment.
  • Collaboration among quantitative researchers, engineers, and traders.
  • Work on machine learning, quantitative research, and large-scale datasets supporting systematic trading.

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