Machine Learning Engineer

A leading buy-side firm, is looking for a Machine Learning Engineer to push the boundaries of how data and models are used in systematic investing. This isn't a research support role - it's about building the ML infrastructure that directly drives trading strategies and PnL.

The Opportunity

• Work with world-class researchers and portfolio managers where every model you build has real market impact.
• Take ownership of the full lifecycle: from messy, high-frequency datasets feature engineering, training and deployment, live inference.
• Shape the firm's ML ecosystem, choosing the right tools and frameworks to deliver performance at scale.
• Tackle challenges you won't find outside finance: billions of data points, low-latency requirements, and models that need to work in the wild, not just in notebooks.

What We're Looking For

• Strong hands-on experience with machine learning engineering at scale.
• Expertise in Python (plus C++/Java a bonus), and familiarity with ML frameworks like PyTorch, TensorFlow, Scikit-learn.
• Experience building production-grade pipelines (Spark, Ray, Kafka, Dask, Kubernetes, cloud).
• Ability to handle large, noisy datasets - financial or otherwise - and turn them into production-ready models.
• Curiosity, pragmatism, and the mindset to solve problems where milliseconds and accuracy both matter.

Why This Firm?

• Direct impact: your work feeds into live trading strategies, not just experiments.
• Buy-side edge: fewer layers, faster decisions, more ownership.
• Top-of-market comp: highly competitive base + performance bonus.
• Culture of excellence: small, high-performing teams where engineers, quants, and PMs work side by side.

If you're a machine learning engineer who wants to operate at the sharp end of finance, building systems that actually move markets, this is the role.