DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.
Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.
We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it’s how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.
As an AI Engineer at DRW, you will be an integral member of a collaborative multi-asset trading team. You’ll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that are central to our business strategy. In this role, you will build critical infrastructure and tooling that leverages AI and ML technology to enhance productivity, efficiency, and discover innovative paths to challenging business and technology problems. Surrounded by cutting-edge technology and experienced trading teams, you will have the opportunity to tackle fascinating new projects while becoming a subject-matter expert in a critical and innovative role.

QUALIFICATIONS:

• Bachelor’s or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 3+ years of experience working with machine learning and artificial intelligence technology.
• Strong understanding of core machine learning and artificial intelligence concepts.
• Excellent programming skills in Python.
• Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models.
• Hands-on experience with MLOps and AIOps infrastructure and tooling.
• Proficient in problem-solving and analytical reasoning.
• Exceptional communication and collaboration skills.
• Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX.
• Experience with Large Language Models, including RAG and fine-tuning techniques.
• Familiarity with compute infrastructure necessary to support operating AI and ML technology.
  The annual base salary range for this position is $175,000 to $300,000, depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus. In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.
  For more information about DRW’s processing activities and our use of job applicants’ data, please view our Privacy Notice at https://drw.com/privacy-notice.
  California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.
  

  LI-BL

• Drive end-to-end development of AI infrastructure and AI driven applications: from initial proof-of-concept to production deployment and ongoing maintenance.
• Collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact opportunities for integrating AI and machine learning into technology and business use cases.
• Implement automated systems for continuous training, validation, and monitoring of models, minimizing downtime and ensuring reliability.
• Provide technical leadership in selecting, integrating, and optimizing AI and ML frameworks, libraries, and tools across diverse hardware and software environments.
• Create and maintain feature pipelines, feature stores, and model stores.
• Develop frameworks to enable scalable, reproducible research.
• Proactively troubleshoot performance bottlenecks, conduct root-cause analyses, and implement solutions to optimize GPU or CPU resource usage.