If you want a top technology job with a hedge fund or systematic trading firm, and you’ve spent your career working in technology for an investment bank, you might be out of luck. Technologists occupying some of the most interesting jobs in the systematic trading space don’t come from banks… they come from top technology firms. More specifically, they come from the tiny number of people working within top technology firms’ research labs.
“The work that’s being done on machine learning in places like Facebook’s AI labs and Google Brain correlates very well with the problems that arise in the systematic space, where they’re working on ecosystems to house their own machine learning needs,” says Luke Thompson, a director at search firm Thurn Partners and a former recruitment manager for trading technology hiring at Morgan Stanley in London.
Thompson declined to comment on the funds and trading firms that are hiring from FAANG labs, but their names are well-known. Two Sigma, Citadel, Jane Street, Virtu, and Point72 are just a few of the funds known to be building out machine learning teams using staff sourced from big tech.
Citadel, for example, hired Li Deng from Microsoft’s research lab to build out an AI team in 2017. Deng has since hired people such as Pusheng Zhang, a former technology lead in Uber’s machine learning lab in Seattle, Wenyi Huang, a former research scientist at Facebook, and Sihang Liang, a former research intern at Google’s AI lab in Mountain View.
Rivals show similar preferences. Two Sigma’s AI team, for example, is managed by Mike Schuster, who joined from Google’s research team in 2018. Schuster, who is still hiring, has recruited people such as Dezhong Deng, a former applied scientist intern at Amazon.
Meanwhile, Virtu has people like Till Varoquaux, a former member of the applied machine learning team at Facebook.
Thompson says FAANG engineers aren’t just needed to work on A.I. that can generate alpha. “Large quant-based asset managers are starting to implement machine learning algorithms across the broader aspects of their business,” he says. “It’s not just about signal generation – firms are building transaction cost analysis frameworks to work out best execution methods, factoring in broker fees and trading costs, for example.” In other words, machine learning is also being used to cut costs.
Would FAANG researchers really want to move to hedge funds? The answer appears to be “yes.” Salaries can be comparable, says Thompson, and while big technology companies will reward you with stock, big funds may give you a share of their profits.
A modified version of this article originally appeared in eFinancialCareers.