Lead Trading Systems Engineer (USA)

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profile Job Location:

Stamford, CT - USA

profile Monthly Salary: $ 175000 - 200000
Posted on: Yesterday
Vacancies: 1 Vacancy

Job Summary

Description

Trexquant is looking for a senior technologist to lead the design and evolution of our core trading research and simulation infrastructure. This role involves architecting and building scalable low-latency systems in Linux environments collaborating closely with quantitative researchers & engineers and driving next-generation simulation and execution platforms.

Responsibilities

  • Design build and enhance core infrastructure systems that support trading research and operations including integration with the firms Order Management System (OMS).
  • Architect and develop a high-performance multi-asset simulation and backtesting platform capable of supporting strategy research backtesting and deployment across equities futures fixed income and derivatives.
  • Evaluate existing systems to identify bottlenecks and implement improvements that enhance scalability performance and security.
  • Collaborate with quantitative researchers and cross-functional teams to ensure the platform accurately models market dynamics transaction costs and execution behavior while aligning technical initiatives with a long-term infrastructure roadmap.
  • Design and optimize scalable data and compute infrastructure for low-latency high-throughput processing of large-scale market data across simulation and production trading.
  • Improve caching time-series management and distributed computation while keeping the simulation & backtesting framework modular and scalable.
  • Oversee the development integration and deployment of systems and tools in C Python and Linux environments.
  • Provide mentorship and technical guidance to engineers and researchers while staying current with emerging technologies and industry best practices.


Requirements

  • Bachelors Masters or Ph.D. in Computer Science Engineering Mathematics or a related STEM field.
  • 6 years of experience building high-performance trading simulation or research infrastructure within Linux environments.
  • Expert-level proficiency in one of these programming languages: C (C17/20) Java or Python with strong knowledge of algorithms concurrency data structures and systems architecture. Preference for C.
  • Experience architecting scalable low-latency high-throughput systems in Linux-based production environments.
  • Knowledge of market microstructure execution systems and simulation or backtesting methodologies.
  • Experience designing or supporting OMS execution or comparable trading infrastructure.
  • Excellent communication skills and ability to collaborate effectively across engineering and research teams.
  • Financial industry experience is a plus but not required.


Benefits
  • Competitive salary plus bonus based on individual and company performance.
  • Collaborative casual and friendly work environment.
  • PPO Health dental and vision insurance premiums fully covered for you and your. dependents.
  • Pre-tax commuter benefits.
  • Weekly company meals

Applications are open for both Stamford and New York City offices the latter with a planned opening in October 2026.

The base salary range is $175000 - $200000 depending on the candidates educational and professional background. Base salary is one component of Trexquants total compensation which may also include a discretionary performance-based bonus. This position is classified as overtime-exempt.

Trexquant is an Equal Opportunity Employer


Required Experience:

IC

DescriptionTrexquant is looking for a senior technologist to lead the design and evolution of our core trading research and simulation infrastructure. This role involves architecting and building scalable low-latency systems in Linux environments collaborating closely with quantitative researchers &...
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About Company

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Trexquant applies quantitative methods to systematically build optimized global market-neutral equity portfolios in liquid markets. Trading signals (Alphas) are developed from thousands of data variables and extensively tested. Strategies dynamically adjust allocations to Alphas depen ... View more

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