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

Key Responsibilities

• Design and implement distributed training pipelines handling high-volume data and complex model architectures
• Develop low-latency inference systems providing real-time, high-accuracy predictions in production
• Optimise and extend machine learning frameworks to improve training and inference performance
• Leverage GPU programming (CUDA, cuDNN, TensorRT) to maximise efficiency
• Automate model experimentation, tuning and retraining in partnership with research teams
• Work with infrastructure specialists to optimise workflows and reduce compute costs
• Assess and integrate emerging open-source tools to enhance ML development and deployment

Skills and Experience

• 5+ years’ experience in machine learning with a focus on training and inference systems
• Strong programming expertise in Python and C++ or CUDA
• Proficiency with PyTorch, TensorFlow or JAX
• Hands-on experience with GPU acceleration and distributed training (Horovod, NCCL or similar)
• Background in real-time, low-latency ML pipelines
• Familiarity with cloud and orchestration technologies
• Contributions to open-source ML or distributed systems projects are advantageous

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: £140,000 - £190,000 + 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.