Voleon

FinTech

MemberofResearchStaff,Optimization

$250–275k Berkeley, California, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Member of Research Staff, Optimization at Voleon”

Industry & Context.

FinTech
Full Job Description

Voleon is a technology company that applies state-of-the-art AI and machine learning techniques to real-world problems in finance. For nearly two decades, we have led our industry and worked at the frontier of applying AI/ML to investment management. We have become a multibillion-dollar asset manager, and we have ambitious goals for the future. Your colleagues will include internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and technology professionals. In addition to our enriching and collegial working environment, we offer highly competitive compensation and benefits packages, technology talks by our experts, a beautiful modern office, daily catered lunches, and more.   As a Member of the Optimization Research Staff, you will work at the forefront of optimization modeling and research at Voleon. Your research colleagues have collectively published hundreds of academic articles in top-tier venues on machine learning, systems, and theory, and we meet regularly to stay current on the latest academic research and share ideas. Founded in 2007 by two leading scientists (see management bios https://voleon.com/management/), http://voleon.com/about/management-2/ Voleon supports a culture of curiosity, collegiality, and creativity.   Financial markets are noisy, high-dimensional, and subject to complex constraints, costs, and uncertainties. We have spent over a decade pioneering scientific advances in applying large-scale optimization techniques to this domain. Your work will focus on the models and optimization systems that help determine how Voleon trades in financial markets, taking into account our predictions and costs, and returns trade decisions in real time. We are interested in efficiently solving this optimization problem (algorithm development and production coding), as well as improving the problem formulations themelves to make better trading decisions. We also implement optimization

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