Top Machine Learning Jobs in London

Lead the design and deployment of recommendation systems, mentor team members, collaborate across functions, and innovate using emerging ML techniques.

Depop seeks a Senior Machine Learning Engineer for the Ranking team to develop and maintain machine learning systems for personalized rankings. Responsibilities include designing training pipelines, collaborating with ML scientists, and optimizing model serving systems.

Build, productionize, and maintain scalable MLOps components and inference workflows for healthcare sensor data. Establish experiment tracking, model lifecycle, reproducibility, monitoring, and compliance best practices. Partner with clinical, data, and platform teams to deploy reliable, monitored ML systems aligned with healthcare-grade reliability and regulatory requirements.

As a Senior Machine Learning Scientist, you'll improve customer outcomes through automated decisioning using ML and statistical modeling, focusing on credit risk modeling and collaborating with cross-functional teams.

Lead the development and deployment of scalable AI systems, mentor junior engineers, and collaborate with teams to solve client challenges.

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.

The Senior Staff Machine Learning Engineer will define and implement ML strategies for content safety and compliance, build scalable systems, and ensure high-quality content evaluation.

The role involves developing advanced machine learning models for game rendering and streaming, conducting R&D, and collaborating on publications.

Paid 6-12 month research internship creating and experimenting with ML models across vision, language, and audio to support game development, own data analysis/creation, and help deploy models at scale while collaborating with research scientists and engineers.

Lead and grow Almedia's London data and ML team (5–10 hires), stay hands-on with code and model reviews, own roadmap for recommendation, personalization and offer-ranking systems, hire and coach staff, and align technical work to business outcomes with senior leadership.

Build, train, evaluate and productionise custom ML and LLM models for customer feedback: fine-tuning pipelines, retrieval/reranking, summarisation, sentiment, evaluation frameworks, and monitoring. Collaborate with engineering, product and commercial teams, mentor colleagues, run experiments, and translate business needs into practical ML solutions.

Build, test and deploy machine learning models and infrastructure for industrial control systems. Collaborate with product teams on customer projects, design scalable ML systems, establish engineering best practices, and develop tooling to support continuous testing and deployment in production industrial plants.

Lead development of a high-performance robotics simulation platform and compiler stack. Design compute infrastructure and dataflows for physics simulation and foundation-model training. Implement JIT/LLVM-based GPU codegen, optimize compile/runtime performance, add differentiable programming support, collaborate with simulation engineers, and contribute to open-source ML compiler projects.

Lead pre-sales technical engagements to architect end-to-end Data & AI solutions on the Databricks Lakehouse. Coach junior architects, design and deliver complex PoCs, drive customer adoption of Generative AI/LLM solutions (fine-tuning, RAG, MLOps), and collaborate with account teams across UK&I and EMEA.

Build and operate production-grade, low-latency ML platforms and feature-serving infrastructure. Design data-intensive streaming systems, create reusable engineering patterns, collaborate with ML/data/backend teams, and mentor peers to improve platform reliability and scalability.

Design and implement enterprise-grade MLOps and LLMOps architectures on AWS, build scalable ML/LLM pipelines, enable CI/CD and observability, enforce governance and security, mentor teams, and guide cross-functional delivery of production ML solutions.

Lead pricing ML for a real-time energy transaction engine: define mathematical formulation, build and ship production-grade models, optimise pricing and portfolio balance, and communicate model behavior to commercial and engineering stakeholders.

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.

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.

Lead the Research team in developing scalable machine learning solutions, mentoring junior engineers, optimizing model training, and collaborating on real-world engineering problems.

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

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.

Lead a multidisciplinary team in machine learning and visual computing, managing timelines, project delivery, and innovation in game rendering technologies.

Lead the execution of production ML systems: build data pipelines, training workflows, evaluation and inference infrastructure, fine-tune large models (LoRA/QLoRA/SFT/DPO), optimize GPU deployments, and integrate ML into products while ensuring robustness, safety, and reliable user-facing performance.

Lead implementation of production-grade ML systems: data pipelines, training/evaluation workflows, scalable GPU inference, model fine-tuning (LoRA/QLoRA/SFT/DPO), deployment, and monitoring. Collaborate with product and application teams to ship reliable, efficient, and safe ML-powered features for real users while managing latency, cost, and robustness constraints.