Machine Learning Engineer

Machine Learning Engineer

Job Description

[Up to c. $300k Comp Package | Hybrid Working]

Role Overview

We’re working with a leading algorithmic trading firm seeking a Machine Learning Systems Engineer to design, optimise, and maintain large-scale ML infrastructure powering advanced trading and research initiatives. This position sits at the intersection of high-performance computing, distributed systems, and applied machine learning - ideal for an engineer who thrives in performance-critical environments and enjoys bridging cutting-edge research with real-world trading applications. You’ll collaborate with data scientists, quantitative researchers, and GPU specialists to develop end-to-end systems for training, deployment, and optimisation of machine learning models at scale...

Key Responsibilities

• Architect and maintain distributed training pipelines for large datasets and complex model architectures, ensuring scalability and fault tolerance
• Build and refine real-time inference systems capable of delivering ultra-low-latency predictions to support live trading and analytics workloads
• Optimise model training and inference performance through GPU acceleration, hardware tuning, and efficient use of libraries such as CuDNN, TensorRT, and NCCL
• Collaborate with research and HPC engineering teams to streamline workflows, boost throughput, and minimise resource bottlenecks
• Develop internal libraries and reusable components to extend and enhance the performance of machine learning frameworks such as PyTorch, TensorFlow, and JAX
• Integrate automation and monitoring into ML workflows, covering model retraining, data versioning, and hyperparameter optimisation
• Evaluate, customise, and deploy emerging open-source tools to strengthen the firm’s ML infrastructure capabilities
• Deep dive into framework internals to identify bottlenecks and implement performance or scalability improvements
• Partner with quantitative teams to translate experimental ideas into robust, production-ready ML pipelines

What You’ll Bring...

• 4+ years’ professional experience as a Machine Learning Engineer, Systems Engineer, or similar role working on large-scale training and inference systems
• Strong software engineering background with proficiency in Python, C++, and/or CUDA
• Demonstrated experience building or tuning low-latency, high-performance ML pipelines for real-time environments
• Deep knowledge of GPU acceleration techniques and distributed training frameworks (e.g. Horovod, Ray, or similar)
• Understanding of end-to-end ML lifecycles - from data ingestion and feature processing to model deployment and optimisation
• Experience working within high-performance computing environments and collaborating closely with infrastructure and platform teams
• Familiarity with orchestration and scaling tools for ML workloads (e.g. Kubernetes, Slurm, or cloud-native equivalents)
• (Preferred) Exposure to financial markets, algorithmic trading, or other latency-sensitive domains

...

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