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Two Sigma

Quantitative finance firm building predictive models

Quantitative Software Engineer: Generative AI

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeNew York, NY, USA

About the Job

Requirements

  • A minimum of 1 year of experience developing and deploying machine learning solutions is required.
  • A Bachelor of Science degree in Computer Science, Machine Learning, or a related technical field is required.
  • Demonstrated success translating research publications into practical solutions is required.
  • Proficiency with natural language processing and deep learning frameworks in Python, such as PyTorch and TensorFlow, is required.
  • Ongoing engagement with emerging trends, research, and applications in generative artificial intelligence is required.

Responsibilities

  • Lead applied research initiatives using generative artificial intelligence technologies.
  • Implement and maintain scalable machine learning and natural language processing systems in data-intensive production environments.
  • Collaborate with modeling partners to prototype and iterate in emerging natural language processing research areas.

Desired Qualifications

  • Three to ten years of experience developing and deploying machine learning solutions, preferably in natural language processing and large language models.
  • Advanced degrees and/or machine learning publications are highly desired.

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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