Machine Learning Engineer
Miami
Job Description
Role Summary
As a ML/Research Engineer at Citadel Securities, you will work closely with researchers to design and build the next generation library for deep learning within the firm. You will combine the best available open-source tools with deep internal expertise in modelling and predicting financial markets. Your work will empower 100+ researchers to iterate faster on their agenda and perform experiments that were not possible before.
Opportunities may be available from time to time in any location in which the business is based for suitable candidates. If you are interested in a career with Citadel, please share your details and we will contact you if there is a vacancy available.
Objectives
- Grow/maintain internal library for deep learning.
- Work with researchers to understand their needs and add new functionality.
- Work with HPC experts to optimize the largest existing workflows and deliver training speed improvements and cost savings.
- Stay up to date with external innovations and tools and make them available for internal use as appropriate.
Skills and Preferred Qualifications
- 2+ years of experience in machine learning and software development.
- Strong engineering skills, including Python, CUDA, C++.
- Experience building distributed deep learning libraries or scaling large distributed deep learning workflows using PyTorch (or similar).
About Citadel Securities
Citadel Securities is a technology-driven, next-generation global market maker. We provide institutional and retail investors with world-class liquidity, competitive pricing and seamless front-to-back execution in a broad array of financial products. Our teams of engineers, traders and researchers harness leading-edge quantitative research and the accelerating power of compute, machine learning and AI to power our analytics and tackle the market’s and our clients’ most critical challenges. Together, we are forging the future of capital markets. For more information, visit citadelsecurities.com.
