1 to 25 of 134 Machine Learning Jobs in the City of London

services and deliver value to its clients. Description Senior Machine Learning Engineer - London This role focuses on training custom models, building robust ML pipelines, and deploying systems at scale from research experimentation through to monitored production services. Design, train, and optimise machine learning models for audio … tasks such as speaker diarization, automatic speech recognition (ASR), and voice activity detection. Build and maintain training and inference pipelines using PyTorch, and related ML frameworks Source, curate, and prepare training datasets; implement preprocessing, augmentation, and validation workflows. Run structured experiments, evaluate model performance, and iterate based on measurable results ...

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 ...

Machine Learning Engineer, you’ll work closely with Data Scientists, Data Engineers and delivery teams to productionise models and build robust, scalable ML systems within client environments. This role is highly hands-on, with a strong focus on deployment, performance and reliability. Key Responsibilities Translating data science models … into scalable, production-ready machine learning solutions. Designing and building end-to-end ML pipelines, from data ingestion to deployment and monitoring. Collaborating with data engineers on data architecture, pipelines and feature stores. Working closely with data scientists to productionise models and improve performance. Deploying, monitoring and maintaining ...

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: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced 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, Triton ...

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: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced 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, Triton ...

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: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced 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, Triton ...

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: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced 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, Triton ...

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: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced 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, Triton ...

Machine Learning Scientist [Analyst/Associate] About the job As a Machine Learning Scientist on the AI team at Cerberus, you’ll work on high-impact projects that combine the pace of a startup with the reach of a global investment platform. Our team partners directly … forecasting, NLP, or optimization. Experience with uncertainty quantification, model evaluation, and statistical inference is highly valued. Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming ...

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: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced 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, Triton ...

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: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced 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, Triton ...

research, development, and deployment of machine learning models for real-time speech enhancement. Responsibilities: Lead the design, training, and optimisation of ML models for speech separation and noise reduction. Provide technical leadership and mentorship to a small, high-performing engineering team. Bridge research and production — ensuring scalable … latency model deployment. Collaborate cross-functionally with software and product teams to integrate ML systems into live products. Contribute to the company’s long-term AI strategy and technical roadmap. Requirements: MSc or PhD in Computer Science, Machine Learning, or Signal Processing. Proven experience in leading or mentoring ...

processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering … workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing ...

deploy, and maintain machine learning systems throughout their entire operational lifecycle Collaborating closely with data science teams to integrate and productionise trained ML models within live systems Hands-on experience implementing and supporting models built with widely used machine learning libraries Proficiency in python Hands … record of guiding, supporting, and developing junior team members through hands-on mentorship and technical leadership. If you are interested in this or other Ml Engineering positions, please contact Gilad Sabari @ gilad.sabari@xcede.com ...

partnering with an early-stage, mission-driven company at the intersection of AI and national defence to appoint exceptional Machine Learning Engineers . This fast-growing organisation is transforming mission-critical combat planning and operational decision-making by building next-generation AI software tools for Western forces. Founded … knowledge with cutting-edge design and software engineering expertise to deliver state-of-the-art SaaS capabilities leveraging NLP, GenAI, Computer Vision, and Reinforcement Learning technologies. Position location (hybrid) : London (Shoreditch) or Paris (Le Marais) We are seeking Machine Learning Engineers who are passionate about using ...

preparing data from multiple sources Designing and implementing data models and algorithms to address business challenges Building, testing, and deploying predictive analytics and machine learning solutions Visualising data and communicating insights clearly to technical and non-technical stakeholders Ensuring data integrity, security, and compliance with internal policies … related discipline Strong proficiency in Python or R for data analysis Experience working with SQL and relational databases Proven experience building and deploying machine learning models Desirable Experience using a major cloud platform Exposure to big data technologies (e.g. distributed data processing frameworks) Experience with data visualisation tools ...

deploy machine learning systems that accelerate decision-making and unlock business value. You’ll design, implement and deploy production-grade AI and ML systems, ranging from NLP pipelines that extract insights from complex documents to integrating models with third-party services to streamline workflows. We’re looking … data-driven products that directly impact investment decisions and portfolio company performance. Drive measurable impact: Partner with internal desks and portfolio teams to integrate ML products into their existing workflows to drive high adoption and value. Move fast and iterate: Work in an agile environment where experimentation, pragmatic engineering ...

Engineer with strong software engineering fundamentals to join our team of domain experts and researchers. You will be responsible for building robust, scalable ML systems that bring our foundation models for retail from prototype to production. Key Responsibilities Design and build production-grade ML infrastructure, including training pipelines, model … serving, and monitoring systems. Collaborate with research engineers to translate experimental models into reliable, maintainable software. Optimise ML systems for performance, scalability, and cost-efficiency in cloud environments (distributed clusters, GPUs). Establish engineering best practices for ML development, including testing, CI/CD, and code review standards. Progression Timeline ...

rolled out across a truly global legal organisation, shaping how advice is delivered to clients in 80+ countries worldwide. You’ll use data and machine learning to enhance legal processes, support strategic decision-making, and deliver forward-looking insights to an international client base. This includes designing innovative … data analysis, machine learning, and model development In-depth understanding of machine learning techniques, with experience delivering end-to-end ML solutions from initial build through to deployment Practical experience with data engineering, including designing and maintaining data pipelines, working with containerisation tools, and developing APIs ...

predictive insights during the action. As a Lead 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. What … 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, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...

predictive insights during the action. As a Lead 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. What … 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, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...

predictive insights during the action. As a Lead 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. What … 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, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...

predictive insights during the action. As a Lead 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. What … 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, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...

Data Analyst About the Role We are looking for a Data Annotator/Data Analyst to support the development and evaluation of AI and machine learning solutions. In this role, you will be responsible for annotating datasets, ensuring data quality, and collaborating with data scientists and engineers … consistent, and accurate annotations following defined guidelines Analyze annotated data to identify patterns, issues, and opportunities for improvement Work closely with data scientists and ML engineers to refine annotation strategies Use annotation tools (e.g., Label Studio or similar) to manage and review datasets Perform basic data processing and validation using ...

Senior ML Engineer £50,000 to 60,000 GBP bonus Hybrid WORKING Location: Central London, Greater London - United Kingdom Type: Permanent Senior Machine Learning Engineer - London/Hybrid Up to £65,000 + DV Bonus | DV Eligible Required | No Sponsorship We are seeking a Senior ML Engineer … join a leading organisation delivering cutting-edge AI and machine learning solutions with real-world impact. You will design, develop, and deploy ML models, including traditional ML and GenAI/LLM applications, supporting national security and regulated sector objectives. This is a London-based role with hybrid working ...