September 20269 min read
How Quant Finance Firms Are Structuring AI Talent Teams in APAC
Artificial intelligence may be changing the talent requirements of quantitative finance firms across APAC, but there is no single model emerging for how these teams should be built, making it increasingly difficult for those hiring to know exactly who to employ.
Some trading firms are creating dedicated AI research labs in strategic Asian locations. Hedge funds are placing AI researchers directly into established teams where their work can contribute to trading strategies and P&L. Elsewhere, firms are making a different structural decision, positioning AI talent either in front office functions or within core engineering.
At the same time, the people these firms would like to hire are changing. Traditional quantitative research and development skills remain highly relevant, but employers also want those capabilities to combine with applied AI expertise. That is opening opportunities for a new generation of AI-native talent and putting quant finance firms into competition with investment management and global technology companies.
Du Qun Yeo, Principal Consultant at Selby Jennings, works with quantitative finance firms and professionals across APAC. Based on current hiring activity, he sees firms making important decisions about where AI sits within their organisations, what they expect these teams to deliver, and how they compete for a limited talent pool.
AI Hiring is building on established quant finance skills
Rather than creating an entirely separate category of financial talent, many AI positions are developing from roles already familiar to quantitative finance firms.
Quant researchers and quant developers remain central to the hiring market. The difference is that employers increasingly want AI capabilities alongside quantitative research, development, and financial modelling expertise.
As Du Qun explains:
With the AI roles that I've been working on, I think it's very specific to my space. It's all a build-up from the typical quant researchers and quant developers’ type of profiles with AI expertise.
This reflects the wider range of quantitative roles Selby Jennings supports across APAC, including quantitative researchers, quantitative developers, traders, portfolio managers, and machine learning specialists across hedge funds, proprietary trading firms, investment banks, asset managers, and financial technology institutions.
Recent Selby Jennings APAC placements have included an ML Scientist / Quant Researcher, Deep Researcher, Quant Developer, Quant Researcher, and other quantitative trading and research positions across Singapore, Hong Kong, China, and Australia.
The change taking place is therefore less about replacing the traditional quant profile and more about expanding it. Firms want people who can connect quantitative methods with newer AI techniques and apply those capabilities to commercial problems.
Two AI team build-out models are emerging
One of the clearest distinctions in the APAC market is how quant finance firms are organising their AI specialists.
Du Qun is seeing two primary approaches: dedicated AI research labs, and AI researchers embedded within established teams. The model a firm chooses can depend on the type of business, its access to specialist talent, and how quickly it expects AI investment to contribute to commercial outcomes.
Dedicated AI research labs
Top trading firms are showing a greater tendency to build dedicated AI research labs in strategic APAC offices.
As Du Qun explains:
Top trading firms tend to be setting up these dedicated AI research labs, where they are building them in certain strategic offices within Asia, depending on where the larger talent pool is.
This approach allows firms to concentrate AI research expertise in locations with access to relevant technical talent, rather than distributing specialists across existing teams.
AI researchers embedded in established teams
Hedge funds are showing a different pattern, with some placing AI researchers directly into existing teams:
Planting AI researchers within established teams, this tends to be happening towards the hedge fund side rather than from the trading side.
Du Qun links this structure to the speed at which hedge funds may want new AI capabilities to move from research into practical application. In some cases, teams are expected to build from zero to one quickly and demonstrate a clear connection between their work and P&L.
Embedding AI researchers can also put specialists closer to existing quantitative research, trading strategies, and commercial objectives.
For firms building AI teams in APAC, this creates an important early decision: whether AI should operate as a concentrated research capability or sit closer to established teams and their commercial priorities.
Front office or engineering? Firms are still deciding where AI belongs
The second structural question is where an AI team sits within the organisation. Du Qun says:
For those types of firms, I think they still view AI as a subsidiary or sub-arm of the technology function.
Placing AI researchers in the front office gives them direct exposure to the business and potentially a clearer connection between their work and trading outcomes.
An engineering-led structure can instead place AI alongside the systems, infrastructure and development capabilities required to put models into production.
The market has not yet settled on which approach will prove most effective:
Both would have different repercussions here. We are still seeing what the effect is in the market, but it remains to be seen.
For firms planning an AI build-out, this makes organisational design an important talent question. Deciding who to hire cannot be separated from deciding where those people will sit, who they will work with, and what business outcomes they will be expected to influence.
Where AI talent is expected to make an impact
How firms structure AI teams is closely connected to what they expect those teams to deliver. Across current quant finance hiring in APAC, Du Qun is seeing demand centred on two broad areas: generating better trading insights and predictive models, and improving the speed and automation of existing quantitative workflows.
AI-assisted research and predictive modelling
One area of demand is using AI to support research and generate insights that can feed into trading decisions.
Firms are looking at how AI specialists can strengthen predictive modelling, help identify new opportunities and expand the range of markets or strategies their quantitative teams can assess.
As Du Qun explains:
Most commonly, I think it's bringing these AI talents in to create AI assistance of insights, as well as predictive modelling to expand the trading universe and P&L avenues.
This puts some AI roles particularly close to front office objectives. Rather than treating AI purely as a technology capability, firms can connect the work of these specialists to trading performance, new strategy development and potential revenue opportunities.
Model training and trading automation
A second area of focus is improving existing quantitative workflows. Du Qun is seeing AI talent brought into teams to improve the accuracy and speed of model training, particularly in functions where machine learning models are already in use.
There is also interest in taking systematic trading automation further. Firms are looking at making automated trading even more automated, in laymen’s terms.
Event-driven trading is one example. Markets can react rapidly to breaking news, public comments, and other forms of unstructured information. Firms are exploring whether advances in AI can help algorithms interpret these events and incorporate them into trading decisions with less reliance on manual intervention.
For experienced AI hires, the focus can extend to advancing machine learning strategies that firms have already spent several years developing. This includes capabilities such as natural language processing and deep learning:
A lot of these experienced AI roles in prop trading firms or hedge funds, or particularly in quant finance generally, are for people who are actually capable of taking established machine learning strategies to the next level.
The requirement for experienced talent is therefore increasingly being applied. Firms are looking for specialists who can build on existing quantitative and machine learning infrastructure, improve how models perform, and connect AI development to practical trading and research objectives.
Why junior AI-native talent is gaining traction
Perhaps one of the more significant developments is the opportunity being created for junior talent.
AI remains a comparatively young area of commercial application, which means years of professional experience does not necessarily correspond directly with depth of exposure to the latest research and methodologies.
Some graduates and early-career professionals are entering the market having worked extensively with AI through academic study and research:
A lot of these roles are also open to juniors now, simply because it is the juniors who are coming in with that trained experience or academic or research experience in academia in the field of AI.
The strongest junior candidates can therefore offer an unusual combination: recent exposure to AI research, strong technical foundations and evidence that they can translate that knowledge into an application with commercial relevance.
Junior AI salary benchmarks in Singapore
Competition for these profiles is reflected in compensation.
The following benchmarks were provided by Du Qun based on junior AI hiring across global technology, investment banking, and hedge fund employers in Singapore.
| Company type | Role | Base compensation |
| Global big tech firm | AI Researcher | SGD 180,000 |
| Global investment bank | AI Quant Researcher | SGD 220,000 |
| Global investment bank | AI Researcher | SGD 180,000 |
| Global hedge fund | AI Quant Researcher | SGD 200,000 |
Source: Selby Jennings market data supplied by Du Qun Yeo. Junior-level Singapore benchmarks. Figures represent examples from the market rather than standardised salary bands. Equity for the global big tech firm is shown as an approximate annualised value taking the vesting period into account.
The numbers also show why base salary alone does not provide a complete comparison.
It is important to note that the AI Researcher at a global big tech example carries a base of SGD 180,000 but can also reach approximately SGD 285,000 in total guaranteed compensation once sign-on, guaranteed bonus, and annualised equity are included.
By comparison, the AI Quant Researcher at a global investment bank has the highest base in the sample at SGD 220,000.
Team positioning differs too. The SGD 220,000 investment banking AI Quant Researcher sits within an AI/ML team in the global markets function, while the SGD 180,000 AI Researcher sits within core engineering. The SGD 200,000 hedge fund AI Quant Researcher is part of an ML trading team.
Those examples illustrate the wider structural trend. Similar AI skills are being deployed in different parts of financial organisations, from engineering through to functions sitting considerably closer to markets and trading.
Quant finance is no longer competing only with quant finance
AI hiring also changes who financial firms compete against for talent.
A quantitative researcher might historically have compared opportunities across hedge funds, proprietary trading firms, banks and asset managers. An AI-native researcher can have a broader set of options, and now a multitude of industries are competing for the same pool of AI native talent. Therefore, compensation needs to extend beyond direct financial competitors.
Technology firms can use equity and other compensation components to create attractive packages. Hedge funds can offer proximity to trading and measurable P&L. Banks can offer access to substantial datasets, established infrastructure, and global markets functions. Each firm therefore needs a clear proposition for why an AI specialist should apply their expertise there.
Du Qun is seeing a corresponding premium for ‘AI builders and AI researchers’, particularly those who can connect research with application.
The ability to demonstrate what a candidate will work on, where the role sits, and how their research will influence the business may consequently become as important as the role title itself.
Competing for AI talent requires speed, but speed alone is not enough
Firms are already responding to increased competition by shortening interview processes.
Candidates can be involved in several hiring processes simultaneously, and a lengthy assessment can leave an employer behind competitors able to reach an offer more quickly.
Du Qun says firms are attempting to keep interview processes lean to lock in candidates early, but there is a trade-off:
In a quest to clear the technical screenings of AI researchers or developers fast and early, I noticed that clients tend to overlook selling themselves.
A process dominated by technical assessments can become transactional. Candidates may complete each stage efficiently without developing a meaningful understanding of the team, its strategy, or why the opportunity is worth choosing over another offer.
Du Qun describes the risk as the candidate experience becoming more process-led rather than relationship-driven. For firms competing for scarce AI talent, the answer is not necessarily to add more stages, but to make each stage work harder.
An effective process should assess technical capability while also giving candidates opportunities to speak with people who can explain the business, team structure, and commercial application of their work. That is particularly relevant when competing with employers outside financial services.
Four considerations for firms structuring AI teams in APAC
Current hiring activity points to four questions for quant finance leaders planning AI buildouts.
A research lab exploring longer-term applications requires a different talent strategy from an embedded team expected to contribute quickly to existing trading activity.
Front office and core engineering structures create different relationships between AI specialists, technology teams and commercial functions. This decision should inform the profiles firms target.
AI-native graduates and early-career researchers can bring recent academic and research exposure, but applied experience remains an important differentiator.
Fast interview processes matter, but firms also need to communicate what candidates will build, who they will work with and how their contribution connects to the business.
Building AI teams for the next stage of quantitative finance
AI hiring across APAC quant finance is developing through several models rather than converging on a single structure.
Dedicated research labs, embedded AI researchers, front office teams, and core engineering functions are all in play. At the same time, employers are assessing a talent pool that increasingly spans traditional quant specialists, experienced machine learning professionals and junior AI-native researchers.
The common thread is application. Firms are looking for talent capable of connecting technical depth with predictive modelling, trading automation, research, and measurable business outcomes.
For employers, defining the structure of the team before going to market can make the search more focused. Candidates need to understand not simply that a firm is investing in AI, but where they fit into that investment and what they will have the opportunity to build.
Selby Jennings works with quantitative finance organisations across APAC to identify and secure business-critical talent across quantitative research, trading, development, machine learning and financial technology.
Our wider quantitative coverage includes investment banks, hedge funds, proprietary trading firms, asset managers and financial technology institutions.
Planning an AI or quantitative talent build-out in APAC?
Contact Selby Jennings to discuss your hiring requirements and current market conditions.