How AI Investment Is Changing Hiring at Trading Firms

By Fynn Johnson, Senior Consultant, Tribus

Trading firms have been using machine learning for years, but the scale of their investment in AI infrastructure is changing.

Recent announcements from some of the industry's biggest firms show where things are heading, with significant investment going into GPU capacity, specialist hardware and the infrastructure needed to support more complex AI research.

And that is creating a different set of hiring requirements.

More AI means more infrastructure

DRW has secured a dedicated NVIDIA Blackwell B300 GPU cluster through QumulusAI, while Hudson River Trading has expanded its relationship with CoreWeave to support its AI research platform.

Jane Street has also become the first customer of chip company Etched, deploying its technology after taking delivery of its first rack. Jane Street also led Etched's $700 million funding round.

The important part from a recruitment perspective isn't just the hardware being purchased.

It's the engineering required to make it useful.

Trading firms need people who can build and maintain the infrastructure around these systems, manage large-scale compute and make sure everything performs reliably.

The demand is moving beyond traditional AI roles

We're increasingly seeing firms looking for engineers with experience across:

• ML infrastructure

• AI platforms

• GPU computing

• Distributed systems

• Data engineering

• Production engineering

The difficult part is finding people who have this experience and understand high-performance or production-critical environments.

A machine learning engineer who has built models is one thing.

An engineer who understands how to build the infrastructure needed to train and deploy those models at scale is a different profile.

Add experience with trading systems, low-latency technology or financial markets, and the candidate pool becomes considerably smaller.

Trading firms aren't the only ones hiring

The competition for these engineers is coming from well beyond financial services.

AI labs, cloud providers and specialist infrastructure companies are all looking for people with experience in GPU infrastructure, distributed computing and ML platforms.

That means firms can't necessarily solve the recruitment challenge simply by increasing the salary.

From what we're seeing, candidates also want to know what they'll actually be building, how much ownership they'll have and whether they'll be working with technology that is genuinely difficult to find elsewhere.

For some engineers, the opportunity to work with specialised hardware and large-scale systems can be just as important as the compensation package.

What we're seeing across APAC

Demand for these profiles is growing across our APAC technology recruitment searches, particularly among larger proprietary trading firms and multi-strategy funds building out their AI capabilities in Singapore and Hong Kong.

There aren't many candidates who combine AI infrastructure experience with a background in financial markets or other performance-critical environments.

That puts a premium on firms being clear about what they're building and moving quickly when they find the right person.

For technology professionals, it also creates some interesting opportunities.

Experience across AI, machine learning, GPU infrastructure, distributed systems and trading technology is becoming increasingly valuable as financial markets firms continue to invest in their AI capabilities.

If you're building an AI infrastructure team at a trading firm, or you're an engineer considering your next move across APAC, get in touch to discuss the market and current opportunities.

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