Why Firms Are Hiring Engineering-Hybrid Quants

Most in-demand quants in finance today are also engineering hybrids, because top firms want people who can work across quant research, development, and infrastructure. For companies hiring quantitative talent, this shift is no longer about technical preference; it is about execution speed, operational risk, and long-term return on hiring investment. 

Over the past decade, demand for quantitative professionals has grown steadily across hedge funds, proprietary trading firms, banks, and asset managers. What has changed is not the need for quants, but the definition of value. Firms no longer gain an edge by hiring specialists who operate in isolation. They gain an advantage by hiring people who can take ideas from research through to live systems and ongoing optimization. 

This evolution has forced hiring managers to reassess how quant teams are structured and how roles are defined. The firms that adapted early are now setting the hiring standard for the broader market. 

Where quant hiring has grown and why it matters 

Quant hiring growth has been strongest in areas where automation, data scale, and execution speed directly influence results. Systematic hedge funds continue to expand teams across equities, macro, and multi-asset strategies, environments where models must be updated frequently and deployed quickly. In these settings, quants who can research, code, and productionize without friction consistently deliver more value than those limited to a single function. 

Proprietary trading firms have followed a similar path. Many now expect quants to work closely with engineering and infrastructure, contributing directly to production systems, performance optimization, and real-time analytics. The separation between research and execution has narrowed significantly as latency, stability, and system behavior have become core performance drivers. 

Banks and asset managers have also reshaped quant hiring. Rather than building isolated models, teams are investing in shared analytics platforms and reusable tools. Risk, pricing, and portfolio construction functions increasingly require quants who can design scalable systems that support multiple users and strategies. 

Machine learning-focused quant roles have expanded across all firm types. These positions almost always require strong engineering capability alongside statistical judgment, as models must operate reliably within complex production environments. 

These patterns align with broader market dynamics outlined in our recent blog on what’s influencing quant talent. Across all of these areas, most in-demand quants in finance today are also engineering hybrids; top firms want people who can work across quant research, development, and infrastructure because this is where performance is created. 

The limits of traditional quant hiring 

Traditional quant hiring models rely on a clear separation of responsibilities. Researchers design models, developers implement them, and infrastructure teams support deployment. While this structure once made sense, it now introduces friction that compounds over time. 

In practice, this model creates several recurring issues: 

• Slow handoffs between research, development, and infrastructure 
• Loss of context when models move from research into production 
• Late discovery of data, latency, or system constraints 
• Fragmented ownership when issues arise in live environments 
• Longer resolution times when performance degrades or systems fail 

From a business perspective, these inefficiencies slow execution and increase operational risk. In fast-moving markets, delays directly affect returns and limits a firm’s ability to respond to change. 

The business advantage of engineering-hybrid quants 

Engineering-hybrid quants fundamentally change how work flows through an organization. Because they understand both research intent and system constraints, they build models with production in mind from the start. This tends to shorten development cycles and reduces the amount of rework required before deployment. 

These hires also improve system stability. By applying engineering discipline early, including testing, monitoring, and performance awareness, hybrid quants reduce the likelihood of failures once strategies go live. When issues do occur, they can diagnose problems across the full stack, from model logic through data and infrastructure. 

Infrastructure investment also delivers stronger returns when hybrid quants are involved. These profiles understand how models interact with compute resources, data pipelines, and latency. As a result, they make better architectural decisions and avoid costly redesigns as platforms scale. 

Just as importantly, engineering-hybrid quants scale better as teams grow. One hire can contribute across multiple functions, act as a technical reference point, and adapt as strategies and systems evolve. 

What this means for a quant hiring strategy 

For hiring managers, the implications are clear. Role definitions need to reflect real workflows, not legacy organizational charts. Rigid distinctions between research and engineering often exclude the strongest candidates. 

Interview processes should test applied capability as well as theory. Code quality, system thinking, and ownership mindset matter as much as mathematical depth. Compensation decisions should reflect impact rather than job title, as hybrid quants often replace multiple handoffs and raise overall team efficiency. 

What we see at Selby Jennings 

At Selby Jennings, we are seeing a clear shift in how firms approach senior quantitative hiring. Mandates increasingly focus on roles that sit across multiple functions, with clients seeking individuals who can influence team structure and technical direction, not just deliver within a narrow remit. 

Our work is driven by long-term relationships with quantitative professionals who have built careers across research, software development, and platform engineering. Many of these individuals are not visible through traditional recruitment channels and are selective about when and why they engage in hiring conversations. 

Because of this access, we are often brought into searches at an early stage, before a role has been fully defined. This allows us to advise on how responsibilities should be scoped, how expectations align with current market supply, and how to position opportunities to attract highly sought-after talent. 

We also see increased emphasis on succession planning and team resilience. Clients are prioritizing hires who can support future growth, contribute to internal standards, and adapt as systems and strategies change. These considerations increasingly shape senior hiring decisions. 

A selection of our notable quantitative placements can be viewed here. 

Accessing the right hybrid quant profiles 

Exceptional engineering-hybrid quants are scarce and rarely visible on the open market. Access requires deep market coverage, long-term relationships, and a clear understanding of what a strong hybrid quant profile looks like in practice. 

If you are hiring quants and want access to candidates who can work across quant research, development, and infrastructure, request a call back from Selby Jennings. We can advise on current market availability, realistic hiring timelines, and connect you with candidates capable of delivering immediate and sustained impact.