T

Trexquant

Quantitative trading via ML models

Trading & Simulation Engineer

Backend EngineeringUpdated on Nov 13
Full-time

About the Job

Requirements

  • A degree in Computer Science or a related field is required.
  • At least 2 years of experience writing production-quality code in Python or C++ (C++17/20) is required.
  • A deep understanding of data structures, algorithms, concurrency patterns, and numeric processing is required.
  • Experience designing, implementing, and optimizing low-latency and high-throughput systems is required.
  • Experience with distributed systems is required.

Responsibilities

  • Design, build, and maintain greenfield infrastructure for quantitative research, backtesting, and strategy deployment, using advanced technologies, tools, and frameworks to develop scalable, high-performance systems.
  • Collaborate with quantitative researchers and senior management to develop technical solutions that enhance research productivity, backtesting accuracy, and trading performance, while optimizing infrastructure for large-scale data processing and complex model computations.
  • Develop and automate data pipelines, research tools, and model deployment workflows, ensuring efficient data access, integration with trading systems, and reliable support for researchers and traders.

Desired Qualifications

  • Experience in alpha/strategy research infrastructure or data pipeline development is a plus.

About the Company

Trexquant

Quantitative trading via ML models

Trexquant is a quantitative finance firm that builds multi-asset portfolios using machine learning. It develops investment and research tools that ingest a wide range of data variables to create complex trading models and strategies, which in turn generate trading signals. These signals guide decisions across global markets. The product works by applying advanced machine learning to large datasets to create predictive models and rule-based strategies, which drive portfolio construction and trading actions through the firm’s platform. Trexquant differentiates itself by emphasizing the breadth of data it uses and the depth of machine-learning driven models to craft multi-asset portfolios, aiming to produce signals that outperform global market conditions. The company’s goal is to generate investment performance that beats benchmarks by using data-driven signals and diversified, multi-asset strategies.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
Stamford, CT
Founded
2012

Recently Posted Jobs

Explore more opportunities at Trexquant

See more jobs