Machine Learning Engineer in London

We are looking for an ML Engineer to work within a growing technology company developing advanced machine learning systems to enable large-scale, computation-driven workflows in a highly complex technical domain. The organisation focuses on translating cutting-edge research into production-grade platforms that support real-world decision-making and experimentation at scale.

As a Machine Learning Engineer focused on scaling, your mission is to build, optimise, and productionise machine learning systems, ensuring models can be trained, deployed, and operated reliably across demanding environments.

This Will Offer You

• Ownership of core machine learning systems used in real-world production settings
• The opportunity to work at the intersection of ML infrastructure, model development, and system design
• Close collaboration with research and product engineering teams
• Exposure to large-scale training, inference, and distributed compute challenges
• High autonomy in a technically ambitious, fast-moving environment
• Competitive compensation and long-term growth opportunities

Your Responsibilities

• Build and maintain scalable training and inference pipelines for modern ML models
• Optimise model performance, latency, and throughput across environments
• Design modular, reusable ML components for internal platforms and tooling
• Translate research prototypes and notebooks into production-ready systems
• Own and improve ML infrastructure components, including data pipelines, distributed compute, and experiment tracking
• Collaborate closely with cross-functional teams to support end-to-end ML workflows

You Will Bring

• MSc or PhD in Machine Learning, Computer Science, Applied Mathematics, or a related field
• Strong Python skills and hands-on experience with frameworks such as PyTorch, JAX, or TensorFlow
• Experience building and scaling ML pipelines in real-world environments
• Familiarity with MLOps tools and practices (e.g. experiment tracking, orchestration, containerisation)
• Experience with modern ML architectures (e.g. Transformers, diffusion-style models, sequence models)
• High ownership mindset, fast iteration speed, and comfort operating in ambiguous, early-stage settings

Nice to have:

• Contributions to open-source ML tooling
• Experience with distributed training, model optimisation, or large-scale serving
• Exposure to post-training scaling or large inference workloads
• Experience integrating ML systems into user-facing products or APIs