Top Machine Learning Jobs in London

Lead organisational ML engineering strategy and architecture for high-stakes programmes. Own end-to-end delivery, drive adoption of platforms and technologies, coach senior engineering talent, shape executive client engagements, and lead technical pre-sales to win flagship AI projects.

As a Data & AI/ML Architect, you will conduct workshops, oversee project delivery, design architectures, and mentor juniors while engaging with clients.

Provides principal-level technical leadership for enterprise machine learning systems supporting a matching platform. Responsibilities include ML architecture, feature pipelines, model development, AWS SageMaker MLOps, low-latency inference, explainability, governance, security, monitoring, drift detection, testing, and deployment automation. The role mentors engineers, establishes engineering standards, and drives scalable, reliable, compliant ML platform capabilities across environments.

Build, deploy, and maintain production ML and AI systems: model packaging, lifecycle automation, monitoring, batch and real-time workloads, and platform improvements while collaborating with data scientists, engineers, and product teams.

The Graduate Machine Learning Researcher will design, test, and implement machine learning models to improve predictive accuracy in sports betting analytics. In a collaborative environment, they will interface with experienced professionals and be involved in R&D from design to implementation, utilizing their mathematical insights.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

Design and evaluate memory architectures for foundation models across modalities. Build production-ready, scalable learning models and algorithms, define memory evaluation metrics, collaborate with researchers and engineers, and guide roadmap decisions for long-horizon model behavior and efficient memory storage/retrieval.

The Senior Machine Learning Engineer will develop and deploy ML models, enhance infrastructure, and collaborate on customer projects, contributing to innovative solutions for carbon emission reduction.

Lead design and delivery of production-grade AI agent services: build LangChain-driven LLM workflows, design distributed microservices, implement testing/evals and monitoring, deploy and maintain models, guide architecture decisions, write production-quality code, troubleshoot live systems, produce documentation, and mentor junior engineers.

Lead ML engineering role bridging deep model research and production. Own end-to-end ML systems, translate research findings into engineering work, turn production regressions into research probes, and set architectural direction. Collaborate with Data & Analytics and Global Delivery, mentor others, and build evaluation, federation, and serving infrastructure with technical autonomy.

10-week summer ML research internship based in Central London. Work on a supervised research project using advanced ML methods, mentored by senior researchers, with access to large compute, culminating in a final presentation. Top performers considered for full-time roles.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Industry Expert who sets the technical bar for advanced AI, ML, systems, or forward-deployed engineering programs. Responsibilities include reviewing deployed projects, leading technical defenses and mock review boards, delivering occasional live sessions, critiquing architecture and tradeoffs, and advising instructors on difficult design decisions. The role requires production-scale engineering experience, senior-level judgment, a public technical footprint, strong English, and comfort with AI tools. It is fully remote and requires approximately 4–10 hours monthly.

The role involves building and operating ML systems and infrastructure for large foundation models, data pipelines, and experiment orchestration in a collaborative environment.

Deploy and optimize LLM-based solutions for real-world government, healthcare, and energy problems. Build scalable data pipelines, productionize models (RAG/agents), monitor performance, engage stakeholders including government officials, and collaborate with cross-functional teams to deliver impactful AI products.

Own end-to-end productionisation of ML models, build and mature the ML platform and serving architecture, integrate system testing and monitoring into training pipelines, automate releases and drift detection, and measure real-world impact alongside data scientists.

Lead identification, development, and global deployment of AI and automation solutions to optimize forms and precedents content operations. Prioritize opportunities, collaborate with cross-functional teams, ensure governance and quality, integrate solutions, report outcomes to leadership, and drive continuous improvement and responsible AI practices.

As a Lead Data Scientist, you will set technical direction, ensure project delivery, manage client relationships, and mentor data scientists.

The Computer Vision Engineer will lead AI initiatives in the defense sector, delivering solutions and mentoring teams while driving technical strategy and capability development.

Lead end-to-end research in computer vision and spatial AI, developing novel algorithms (metric learning, 3D geometry, vision-language) and moving prototypes to production-grade, real-time systems. Shape research roadmap, publish at top venues, mentor junior researchers, and collaborate with engineering to deliver deployable spatial intelligence.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Own and operate end-to-end ML infrastructure: build and maintain Airflow pipelines, CI/CD and IaC for model training/deployment, manage MLflow registry, deploy to AWS and edge devices, implement monitoring/data-versioning, ensure HIPAA/SOC2 compliance, and support reproducible, reliable ML in production.

Research, prototype, and deploy ML models for physiological sensing (radar, audio, other signals). Design experiments, implement production-grade models for cloud and on-device inference, optimize for constrained compute, collaborate with clinical and engineering teams, and contribute documentation and regulatory evidence.

The Staff ML Performance Engineer optimizes ML inference for edge devices, profiles performance bottlenecks, and implements improvements across systems while collaborating with model developers to enhance on-device performance.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.