1 to 25 of 183 Machine Learning Jobs in the City of London

access services quickly and safely. Our Applied Scientist team consists of about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely … Have strong experience in machine learning and computer vision. Have a strong record of successfully delivering high-performance ML-driven products. Have a deep understanding of machine learning theory. Have strong coding skills in Python and PyTorch. Care about building fair and cutting-edge machine ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

models that make sense of high-dimensional biological data. The successful candidate will work alongside bioinformaticians, software engineers, and lab scientists to build scalable ML systems that directly influence the discovery pipeline. This is a hands-on role with ownership - from prototyping models to productionising them in a fast-moving … teams to translate experimental data into actionable insights Optimise models for performance, scalability, and real-world usability Contribute to the design of their ML infrastructure and tooling stack What They’re Looking For 4+ years experience in machine learning, data science, or AI engineering Strong Python skills (NumPy ...

products, platforms, and operational environments. Responsibilities Define and guide machine learning strategy across multiple portfolio companies Help AI and engineering teams move ML initiatives from proof of concept into scalable production systems Align machine learning initiatives with product, commercial, and operational goals Advise on architecture across … operating models to improve execution maturity Qualifications 10+ years of experience across machine learning, AI, or data-driven environments Experience leading ML teams within production-scale businesses Strong understanding of LLMs, MLOps, AI platform architecture, and scalable ML systems Experience operating within high-growth SaaS, AI-native, TechBio ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

mission-driven technology company operating in the life sciences domain is seeking a Principal Scientist - hands-on with either ADMET or Structural Biology modelling, ML engineer to lead the technical direction for ADMET modeling efforts within its drug discovery platform. The organisation enables collaborative model development across partner organisations while … machine learning, computational chemistry, and applied research to advance foundational model applications in drug discovery. You'll be the technical authority on ML architecture, experimentation, and strategy, while focusing specifically on data security and privacy. You will also collaborateclosely with leadership and mentoring other engineers and researchers. While ...

sits at the intersection of Data Engineering and Machine Learning Engineering, with a strong focus on building and productionising end-to-end ML pipelines. You will be working on real-world applications of neural networks, enabling scalable feature engineering, model training, and inference in production. Key Responsibilities Build … productionise feature engineering pipelines for ML models (neural networks) Develop and manage training and inference workflows at scale Deploy and monitor machine learning models in production environments Collaborate with data scientists and engineering teams to optimise model performance and reliability Contribute to best practices across MLOps and pipeline ...

Software Engineers to define data needs and ensure seamless integration into the product ecosystem. Utilize Python (Django) or Go to support the deployment of ML models. Requirements for the role: Strong grasp of statistics and modelling with an ability to derive actionable insights from patterns. MLOps experience Proven history … shipping ML products, ideally within demand prediction, computer vision, or optimization. Strong experience with Time Series Forecasting, Sequence Modelling, Signal Processing or Quantitive Analysis Proficient in Python and SQL. Experience with frameworks like PyTorch, Spark, or Airflow is highly desirable. Familiarity with AWS (SageMaker, Lambda) and CI/CD automation. ...

available £45k to £95k DoE plus 15% clearance bonus Must be willing to be full-time on-site in Central London Skills required in machine learning, GenAI, NLP, Customer Engagement/Consultancy Who are we? We are recruiting Junior, Senior and Lead Data Scientists with AI specialism … look forward to hearing from you. KEY SKILLS: Data Science/Data Scientist/AI Engineer/AI/Machine Learning/ML/NLP/GenAI/Stakeholder Engagement/Customer Engagement/AWS/Azure/Cloud/Docker/Kubernetes/CI/CD/ ...

defence environments. This is an opportunity to work at the cutting edge of data science, focusing on GenAI, large language models, and cloud-based ML deployments, while also taking on leadership responsibilities within a growing team. Role Responsibilities Lead day-to-day technical delivery across data science and ML projects … data governance and security best practices Desirable Skills Experience using GPUs in cloud environments Knowledge of MLOps, CI/CD pipelines, and monitoring ML models Experience building APIs and working with modern DevOps practices Eligibility Requirements Due to the nature of the work, candidates must: Hold active DV clearance ...

Cloud/AI/ML/Transformation/Agile Role: Project Manager (Contract) - AI/Machine Learning Programme Day Rate: Up to 585 a day (Inside IR35) Contract Length: Until March 2027 (extension likely) Location: Hybrid - 2-3 days per week onsite (London) Start: ASAP Overview … looking for an experienced Project Manager to support the delivery of a machine learning proof-of-concept within a large-scale operational environment. This is a high-impact role focused on stakeholder coordination, system integration, and delivery of AI-driven insights, with strong potential to expand into broader ...

Engineer, Alphafold, Boltz, Chai, Drug Discovery | B2B AI Start-up | 3 Days PW in London | Up to £130,000 Plus Benefits As an ML Engineer , you won’t just be a cog in a machine; you will be a foundational member of the team, designing cutting-edge algorithms … . Build: Create production-quality software, integrating structure-based algorithms into a global platform. Collaborate: Work at the intersection of physics, chemistry, biology, and ML to translate scientific needs into product reality. Own: You will have the freedom to drive innovation and shape the company’s technological framework from ...

, optimisation and operational research techniques Strong Python expertise with experience delivering production‐quality data science software Experience working with cloud platforms and modern ML tooling Strong software engineering discipline including testing, CI/CD and version control Ability to structure complex business and technical problems and propose pragmatic solutions … resilience and adaptability in fast‐moving environments Strong commitment to delivering measurable business value Your experience: Master’s degree or greater in data science, ML, or operational research, or 6+ years of highly relevant industry experience (required) 4-6 years working on production ML or optimization software products at scale ...

+ benefits Do you want to lead a world-class data science function at a high-growth organisation where AI, machine learning and advanced analytics sit at the heart of commercial strategy? We're looking for a Head of Data Science who's passionate about building and scaling … Experience with distributed computing and cloud platforms such as AWS, Azure Synapse or Snowflake. Familiarity with data transformation tools (Databricks, Azure Data Factory) and ML platforms (Azure ML). Strong commercial acumen with the ability to translate business problems into scalable, impactful solutions. A track record of influencing at senior ...

technical specifications Required Skills & Experience Great experience combining data science with software engineering Strong expertise in machine learning, with focus on causal ML and probabilistic modelling Experience developing and implementing knowledge graphs Proficiency in scaling AI solutions from concept to production Working knowledge of backend systems, data pipelines … processes Familiarity with cloud platforms, particularly Microsoft Azure Understanding of microservices architecture and distributed systems Experience with DevOps practices for AI/ML workflows (MLOps) Strong programming skills in Python and related data science libraries Demonstrated ability to work within solution architecture frameworks Preferred Qualifications Experience with multiple cloud providers ...

Lead Machine Learning Engineer £Up to £125000 GBP Hybrid WORKING Location: Central London, Greater London - United Kingdom Type: Permanent Reference: AMC/ERO/Lead Machine Learning Engineer Postcode: SW1 JBG81_UKTJ click apply for full job details ...

predictive insights during the action. As a Principal Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. For this role we offer … approach with 2 days a week onsite in Osterley office . What" you'll "do: " You'll "be the technical lead for a critical ML domain (e.g.," live sports insights and personalisation , real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture ...

predictive insights during the action. As a Principal Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. For this role we offer … approach with 2 days a week onsite in Osterley office . What" you'll "do: " You'll "be the technical lead for a critical ML domain (e.g.," live sports insights and personalisation , real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture ...

predictive insights during the action. As a Principal Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. For this role we offer … approach with 2 days a week onsite in Osterley office . What" you'll "do: " You'll "be the technical lead for a critical ML domain (e.g.," live sports insights and personalisation , real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture ...

largest real-time fraud detection platform to craft solutions for our enterprise customers. About the role: Your experience with data analysis, statistical modelling, and machine learning will lead to immediate real-world impact in the form of lower customer friction, reduced fraud losses and as a result, increased … machine learning model development, evaluation, and production deployment, with familiarity in MLOps principles to build scalable and standardised workflows and implement effective ML monitoring systems Proven ability to create polished presentations and effectively communicate insights to customers with attention to detail Have extensive multi-tasking and prioritisation skills. ...

clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity … needs of GS engineers. Be part of our continual drive to innovate and uplift our platforms to take advantage of new technologies (such as machine learning, cloud platforms) Provide technical guidance and support to other team members and stakeholders. Stay up to date with the latest industry trends ...

ML Ops Engineer Role Overview Were hiring an ML Ops Engineer to own the reliability, scalability, and operational integrity of our machine-learning systems in research & production. This role sits at the intersection of data engineering and ML infrastructure: youll design and operate data pipelines that feed models … with clear SLAs, reproducibility guarantees, and observable behaviour. This is not a research role; it is a hands-on engineering role focused on making ML systems work reliably at scale. What Youll Work On ML lifecycle infrastructure Productionizing models: packaging, deployment, versioning, and rollback Designing CI/CD pipelines ...