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Two Sigma

Quantitative finance firm building predictive models

Quantitative Software Engineer: Techniques Engineering

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNew York, NY, USA

About the Job

Requirements

  • A Bachelor of Science degree in Computer Science, Mathematics, Physics, or a related technical subject area.
  • At least 1 year of experience is required; 7–15 years of experience is preferred.
  • Experience in quantitative software engineering.
  • Strong software engineering skills using Python, AI coding tools, version control, testing frameworks, and continuous integration and continuous delivery practices.
  • Hands-on experience building high-throughput GPU machine-learning systems, including low-level performance optimization, batching, scheduling, orchestration, and training infrastructure.
  • Experience collaborating across research and engineering functions and applying appropriate rigor from prototype-stage code to production systems.

Responsibilities

  • Partner with machine-learning researchers to explore model architectures, training methodologies, large language model and agentic engineering, and experimental objectives, translating modeling needs into engineering priorities.
  • Translate prototypes into reusable, maintainable, production-ready code so research insights scale into operation.
  • Design and optimize data-loading and pipeline infrastructure for training and inference at scale, including GPU utilization, batching and bin-packing strategies, and other large-scale compute optimizations.
  • Extend core research libraries to support agentic workflows, integrate modern agent frameworks, and maintain a high quality bar for AI-assisted or AI-generated code and output.
  • Lead structural improvements to keep a growing, multi-contributor codebase coherent, testable, and maintainable as usage and team size scale, evolving internal libraries toward open-source-caliber engineering practices.

Desired Qualifications

  • Experience with CUDA.
  • Experience building on and extending leading machine-learning frameworks such as PyTorch, TensorFlow, or Hugging Face.
  • Experience using large language models and agent frameworks to automate research and engineering workflows reliably and usefully in practice.
  • Experience with agent evaluation frameworks, reinforcement learning from human feedback, reinforcement learning from verifiable rewards, policy optimization, or synthetic data generation.

About the Company

Two Sigma

Quantitative finance firm building predictive models

Two Sigma is a financial sciences firm that uses data analysis, rigorous inquiry, and invention to tackle complex problems in investment management, securities, private equity, insurance technology, and venture capital. Its product approach centers on building sophisticated predictive models powered by extraordinary computing power and vast data. Modelers and engineers test ideas with information and iterative improvement to drive progress. Compared with many peers, Two Sigma differentiates itself through a data-driven, model-centric investment process that relies on large-scale computing and extensive datasets rather than relying on traditional methods alone. The company aims to solve the toughest challenges in its fields and advance investment outcomes by continuously refining predictive models through experimentation.

Company size
1,001-5,000
Company stage
Not listed
Total funding
$378.2M
Headquarters
New York City, New York
Founded
2001

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