- Professional
- Office in New York City
About Magnetar
Magnetar is a leading global alternative asset manager with $22B+ AUM as of May 31, 2025. We are based in Evanston, IL—just north of Chicago and easily accessible by the “L”—with additional offices in New York, London and Menlo Park, CA.
Grounded in two decades of investing experience, we aim to generate consistent performance for our investors by identifying differentiated investment opportunities and building scalable businesses across our core strategies: Alternative Credit and Fixed Income, Systematic Investing, and Venture.
This year, we proudly celebrate our 20th anniversary—a testament to our legacy of innovation, expertise and commitment to our clients and team.
Magnetar employees are among the best in the business at what they do. Our employees thrive on intellectual curiosity, collaboration and identifying creative solutions to complex problems. Our team is made up of highly skilled, passionate professionals who care deeply about delivering results for our investors, our firm and one another. We are proud to foster an inclusive, welcoming environment that empowers each employee to succeed.
At Magnetar, our commitment to our employees matches our dedication to our work. We offer top-tier benefits including comprehensive health, dental and vision insurance, a 401k match, competitive paid time off, wellness programs, daily lunches, and professional development through Magnetar University. We are also passionate about giving back, supporting financial education and community engagement through our Foundation and various volunteer initiatives.
We hope you’ll consider joining us as you explore a career at Magnetar!
Magnetar Capital, LLC seeks Machine Learning Engineer at its facility located at 10 E. 53rd Street, New York, NY 10022.
JOB DESCRIPTION:
Design, develop, and implement machine learning models and algorithms to automate various back-office processes. Engage in work that directly impact the efficiency of operations. Collaborate closely with our infrastructure engineers to ensure that solutions are scalable, secure, and integrated seamlessly into our existing systems. Develop and deploy machine learning models to automate back-office processes, such as trade reconciliation, reporting, compliance checks, and other operational tasks. Work closely with infrastructure engineers to integrate machine learning solutions with existing systems, ensuring scalability and security. Utilize Python, Snowflake, and Databricks to develop data pipelines, preprocess data, and implement machine learning algorithms. Continuously monitor and optimize machine learning models to improve accuracy and efficiency. Collaborate with cross-functional teams to identify areas where machine learning can add value and propose innovative solutions. Stay up to date with the latest advancements in machine learning and apply them to improve our processes. Up to 25% domestic travel required.
REQUIREMENTS:
This position requires a Master’s degree, or foreign equivalent, in Computer Science, Data Science, Machine Learning, Information Engineering, or in a related field, plus 2 years of experience as a Machine Learning Engineer, Software Engineer, or related occupation. Additionally, the applicant must have employment experience with: (1) Applying AI techniques to generate investment theses, automate workflows, and produce quantitative model signals; (2) Performing sentiment analysis, conducting model fine-tuning and evaluation, and executing information extraction from unstructured text data; (3) Designing and implementing algorithmic approaches that drive strategic decision-making and optimize investment performance; (4) Constructing and managing robust data frameworks and pipelines to ensure efficient integration and accessibility of critical data; and (5) Developing and applying comprehensive models for investment analysis, risk assessment, and portfolio optimization.
RATE OF PAY: $139,506-$225,000 per year
Disclaimer
The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified. All personnel may be required to perform duties outside of their normal responsibilities from time to time, as needed.
