Real-Time Quant Systems Software Engineer

Referment

New York (NY)

Presencial

USD 120 000 - 180 000

Tempo integral

há 5 horas
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Resumo da oferta

Referment is seeking talented software engineers to work on technically demanding projects at the heart of systematic investing. You will design and build software for quantitative research and trading, developing distributed systems that process real-time data and support live research and trading decisions.

Collaborate with researchers and other developers to translate complex requirements into reliable, scalable systems, and tackle performance and scale challenges where engineering choices

Qualificações

  • Hands-on software development with strong CS foundations.
  • Excellent analytical and quantitative problem-solving ability.
  • Record of high achievement in CS, math, engineering or related field.
  • Ability to collaborate on complex technical problems and design decisions.

Responsabilidades

  • Design and build software for quantitative research and systematic trading.
  • Develop distributed systems that analyse and respond to real time data.
  • Create tools for advanced mathematical modelling and statistical analysis.
  • Work with researchers and other developers to translate complex requirements into reliable, maintainable systems.
  • Investigate performance and scale challenges where engineering choices directly affect analytical capability.

Conhecimentos

Software development
Algorithms
Data structures
Software design
Analytical thinking
Mathematics

Formação académica

B.S./M.S. in Computer Science or related technical field

Descrição da oferta de emprego

Referment is seeking talented software engineers to work on technically demanding projects at the heart of systematic investing. You will design and build software for quantitative research and trading, developing distributed systems that process real-time data and support live research and trading decisions.

Collaborate with researchers and other developers to translate complex requirements into reliable, scalable systems, and tackle performance and scale challenges where engineering choices

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