Quantitative trading firms are increasing investment in academic research and AI laboratories to win access to machine-learning talent amid a tightening market, with firms like Qube Research & Technologies, G-Research, CFM, and Jump Trading funding PhD programs and launching dedicated AI labs.

First-year compensation for machine-learning PhDs is extremely competitive, with reported ranges from about $450,000 to $650,000 and some packages reaching $800,000, reflecting fierce demand for talent.

Qube Research & Technologies has launched QRT Labs, funding more than 70 PhD and postdoctoral researchers, while G-Research supports dozens of researchers and PhD students through university partnerships.

CFM, Paris-based and managing roughly $35 billion, has created a machine-learning laboratory focused on long-term academic research to identify AI advances that could strengthen systematic strategies.

Firms compete not just on pay but also on computing resources, research independence, and access to cutting-edge AI tools.

The recruitment push highlights the growing importance of machine learning in systematic investing, emphasizing competition over compensation, infrastructure, independence, and advanced model development.

Beyond QRT and CFM, other firms like G-Research, Jump Trading, and Hudson River Trading are funding PhD programs and partnerships with universities to develop advanced trading models.

JPMorgan, XTX Markets, and Optiver have also established dedicated AI research operations, with Jump Trading expanding academic partnerships to bolster recruitment.

Talent mobility is rising between finance and tech, underscoring ML’s growing role in systematic investing, with examples including researchers moving between OpenAI, Anthropic, Google DeepMind alumni, and quant firms.