Citadel hires AI researchers to grow quant team

Citadel is recruiting artificial intelligence researchers from labs including Google DeepMind to expand its quantitative investing operation and has appointed Alexey Poyarkov to lead a new systematic equities team.

Navneet Arora, head of Global Quantitative Strategies, said the firm’s quantitative unit comprises about 180 staff across financial research and data engineering and is planning double-digit annual headcount growth over the coming year. Poyarkov joined from TGS Management and will focus on systematic trading strategies across global equities.

Citadel is continuing to hire recent university graduates while widening its search to researchers working at advanced AI organisations. The firm is seeking candidates who can develop investment concepts and then use machine-learning tools to convert those ideas into executable trading strategies.

“The growing use of AI is changing the skill set required by quantitative investment teams rather than eliminating the need for human researchers,” Arora added. He noted that machines are taking on more coding and technical tasks, making the ability to identify investment opportunities and direct automated systems toward them more important.

The firm is expanding quantitative work beyond equities, with plans to build capabilities in futures and volatility trading. Citadel manages roughly $76 billion across equities, fixed income and commodities and maintains multi-billion-dollar allocations to quantitative strategies. Its Tactical Trading fund, which blends fundamental equity analysis with quantitative methods, returned about 24.7% in the first eight months of 2026 and has an annualized return near 20% since its 2008 launch, according to people familiar with the results.

Hedge funds and proprietary trading firms are competing with technology companies, including OpenAI and Anthropic, for researchers with advanced machine-learning expertise.

Poyarkov’s hire is intended to scale Citadel’s systematic capabilities and bring experienced practitioners into teams that will pair machine learning with traditional investment analysis.