1 to 25 of 136 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 ...

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 ...

machine learning platforms in production Strong background translating data science work into reliable, real-world systems Deep hands-on experience with modern ML frameworks and tooling Expert-level Python engineering and strong software design skills Proven experience building cloud-based platforms e.g. Azure Strong background in containerised environments … Experience leading technical projects and mentoring engineers Comfortable working in fast-moving, client-facing environments If you are interested in this or other Senior Ml Engineering positions, please contact Gilad Sabari @ gilad.sabari@xcede.com ...

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 ...

systems that optimise ad selection, campaign performance, and creative personalisation at a global scale. You will work in a highly collaborative environment with ML scientists and software engineers to deliver impactful solutions for the advertising domain. In this role, you will: Design, implement, and maintain large-scale machine … engineering, model training, validation, and deployment. Build real-time and batch data processing systems to support ad targeting, campaign optimisation, and experimentation. Collaborate with ML scientists and software engineers to integrate ML models into production systems and deliver measurable business impact. Develop APIs and services that enable ML-driven advertising ...

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 ...

Machine Learning Engineer | Early Stage Deep Tech Start Up | London I am partnered with an early stage, London based start up that is building an AI native intelligence layer for private market investing. The platform transforms fragmented documents, deal histories, and proprietary investment data into structured, searchable intelligence … private equity, venture capital, and private credit teams. They are hiring a Founding Machine Learning Engineer to join at early stage, with real ownership over both the core technology and the product direction. This role suits someone with a strong academic background from a leading university , alongside clear ...

Title: AI/ML Research Engineer (London, UK) Location : Must be based London, UK Employment Type : Permanent Work Model : Hybrid (London) Introduction: Our client (international) is a collection of engineers and designers who want the world to connect beautifully. Their team first came together to build one of the world … every day and they're putting this knowledge to work, engineering and designing better ways to connect. They are establishing a new AI/ML engineering team in London to extend these capabilities. This is a foundational role, you will be among the first hires, helping to shape the technical ...

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 ...

Learning Engineer with strong experience in scaling training and inference pipelines for modern foundation models. You’ll work at the intersection of ML research, infrastructure, and product engineering - turning cutting‐edge model code into scalable, reliable systems used in real‐world applications. This is a high‐ownership role … latency, memory usage, and throughput Productionize research code into robust, repeatable systems Work closely with researchers to convert exploratory notebooks into production frameworks Own ML infrastructure components — data loading, distributed compute, experiment tracking Design modular, reusable ML components used across the engineering org Requirements MSc or PhD in Machine ...

Lead ML Engineer: Up to 145k Xcede has just started working with one of the leading applied AI companies in the UK. If you want to make a real meaningful impact on the environment this is the job for you! This senior technical leadership role is focused on building … confidence, enabling customers to reach their objectives while aligning both engineering teams and business leaders. If you are interested in this or other Lead Ml Engineering positions, please contact Gilad Sabari @ gilad.sabari@xcede.com ...

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. ...

developers and researchers. 🛠️ Skills and Experience 5+ years of experience in software engineering, with a strong focus on AI and Machine Learning (ML). Expert-level proficiency in Python and deep learning frameworks, particularly PyTorch. Proven experience building and deploying applications with LLM APIs such as OpenAI … MacBook and Apple Display provided. Team: Become part of a supportive and passionate team environment. If you are driven to redefine the capabilities of machine learning and eager to make a significant impact, Ultralytics offers an exceptional career opportunity. ...

that push the boundaries of AI integration in cloud services. Thought Leadership and Client Engagement • Provide architectural guidance to clients on incorporating GenAI and machine learning into their cloud[1]native applications and architectures. • Conduct workshops, briefings, and strategic dialogues to educate clients on AI benefits and applications … development. • Technologies: Proficiency in Python, Java (and/or Golang), and Spring; expertise in AWS, Azure, Google Cloud; Kubernetes and containerization. • AI Expertise: Advanced machine learning algorithms, GenAI models (e.g., GPT, BERT, DALL-E, GEMINI), NLP techniques. • Big Data: Experience with Hadoop and Spark. • Ethics: Knowledge ...