Top Machine Learning Jobs in London

Develop machine learning models for real-world physics problems, optimize model performance, implement distributed training, and mentor less experienced colleagues.

As a Principal Machine Learning Engineer, you'll develop and deploy advanced speech systems, mentor teams, and define technical vision for ML, focusing on innovation and excellence in model management.

Work with the ML team on a focused internship project involving on-device ML: optimize computer vision models for mobile, fine-tune and deploy multimodal vision-language models, develop data-centric ML systems, and improve model robustness across real-world edge environments. Receive mentorship and deliver experiments to production-aligned evaluation.

Own and evolve the platform infrastructure for deployed ML and NLP systems: deploy and monitor models via Azure ML and AKS, build orchestration pipelines (Dagster), ensure observability and Responsible AI/GDPR compliance, and support scaling across multi-tenant local authority clients while advising across the organisation.

Build and operate MLOps platform components: pipelines, model registries, deployment, monitoring, drift detection, and retraining. Collaborate with Data Science and Data Engineering to establish reproducible, scalable ML lifecycles, governance, and CI/CD for production ML services, and mentor junior engineers.

Design, build, and optimize intelligent systems using AI techniques to solve real-world problems. Responsibilities include data processing, model development, and collaboration.

As a Machine Learning Research Engineer, you will design and implement systems for training and evaluating large language models, collaborate with researchers, and contribute to scalable ML applications in an academic setting.

Design, train, evaluate, and deploy predictive transportation and delivery ML models (primarily XGBoost). Perform EDA, feature engineering, experiment design, and statistical evaluation. Build production ML pipelines and MLOps practices on Google Vertex AI. Collaborate with data engineering and supply chain stakeholders to translate operational needs into scalable solutions.

Lead and own the ML function end-to-end for Rosso: set ML strategy, hire and develop ML engineers, build operating systems (cadences, monitoring, incident review), drive ML-software collaboration, and be accountable for ML outcomes across forecasting, pricing, optimisation, and classical ML.

Lead pricing ML for Rosso: define technical direction, build production-grade price-optimisation and probabilistic imbalance models, bridge modelling and MLOps, and communicate model decisions across teams.

Lead and own tem's ML function (Rosso): set ML strategy, hire and develop ML engineers, establish operating systems (monitoring, incident reviews, prioritisation), and drive close collaboration with software engineering to deliver forecasting, pricing, and optimisation models that meet business outcomes.

As a Lead ML Ops Engineer, you will define standards for ML delivery and operations, shape ML systems engineering, and influence practices across teams.

Design and own machine learning platforms for pricing technology, streamline workflows, and ensure reliable model performance at scale.

The role requires developing ML and quantitative systems using financial time series data, deploying models, and improving signal evaluation for investors.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

As a Staff/Principal Machine Learning Engineer, you will optimize and scale multimodal inference systems, ensuring high performance and reliability in production environments.

As a Machine Learning Engineer, you will deploy AI/ML systems, translate client needs into solutions, and engage with users to provide support and improvements.

As a Staff ML Research Scientist, you will develop advanced ML models for drug discovery, integrating them into products and mentoring junior researchers.

As a Machine Learning Engineer, you will develop production-ready ML solutions, collaborate with multidisciplinary teams, and contribute to Defence and National Security challenges.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

As an AI/ML Engineer, you will design and develop AI-powered applications, integrate AI features into BPM systems, and collaborate with teams to improve operational efficiency through innovative AI solutions.

The Data Engineer role focuses on developing predictive models and enhancing data engineering practices to support business objectives. Responsibilities include writing clean code, model evaluation, and collaboration with a global team to improve reporting and campaign management.

The Senior Data Engineer designs, maintains, and optimizes ML applications and data pipelines, ensuring compliance and quality while mentoring junior engineers.

As a Senior Data Engineer, you will design and maintain ML applications, manage workflows, build data pipelines, and mentor junior engineers within a global team.