1 to 25 of 121 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 ...

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 ...

Machine Learning Engineer £65,000 GBP Onsite WORKING Location: Central London, Greater London - United Kingdom Type: Permanent An opportunity is available for an experienced Senior Machine Learning Engineer to design, build and operationalise advanced machine learning solutions that directly support UK national security objectives ...

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 ...

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 ...

products from inception, working across the full data ecosystem. This includes developing application-specific data pipelines, building CI/CD pipelines that automate ML model training and deployment, publishing model results for downstream consumption, and building APIs to serve model outputs on-demand. The role requires close collaboration with … data scientists and other stakeholders to ensure ML models are production-ready, performant, secure, and compliant. Key Responsibilities Design, implement, and maintain scalable ML model deployment pipelines (CI/CD for ML) Build infrastructure to monitor model performance, data drift, and other key metrics in production Develop and maintain tools ...

with speed, accuracy, and control, gearing up for 4x growth in 2026, it has never been a more exciting time to join! As Senior ML Engineer, you’ll build the production systems that make this possible, ingestion pipelines, graph-based orchestration, and integration of NLP, LLMs, and ML components into … take full ownership of projects, shape the technical direction from the ground up, and grow your career. Stack: Python, API design, Neo4j, Graph schemas, ML/LLM integration/deployment What do you need? Strong backend engineering experience with Python Experience building production ML/NLP systems/deploying ...

AI. Conduct exploratory analysis to understand user behavior, discover optimization opportunities, and develop behavior models to inform product enhancements. Design and deploy data/ML instrumentation to extract insights and optimize the product experience. Provide strategic product guidance through data-driven recommendations, experimentation insights, and root cause analyses. Build … implement, and monitor key performance metrics for AI-driven product features. Stay up to date with industry advancements in data processing and AI/ML, and introduce best practices into the organization. Requirements 7+ years of experience in data science, machine learning, and AI development across structured ...

quality, timely data availability. What you will do Design, build, and maintain scalable, cloud-based data pipelines and architectures to support advanced analytics and machine learning initiatives. Develop robust ELT workflows using tools like dbt, Airflow, and SQL (PostgreSQL, MySQL) to transform raw data into high-quality, analytics … data ingestion from third-party APIs, model data using dbt, and support machine learning workflows for asset pricing and prediction using Azure ML Studio. This includes ELT processes, data modeling, running predictions, and storing outputs for downstream analytics. Your Experience We’re a small, high-impact team with ...

demonstrated through academic study or professional experience Confident using Python and standard machine learning libraries and frameworks A solid understanding of core ML techniques and appropriate use cases Experience delivering ML-driven features such as demand forecasting, optimisation models, or computer vision systems Exposure to working within cloud … statistics for analysing and interpreting complex datasets An interest in developing broader backend or server-side engineering skills to support the deployment of ML components into production systems If you are interested in this role or other Data Scientist opportunities, please contact Gilad Sabari — gilad.sabari@xcede.com ...

analysts, coaches, and performance staff to understand requirements and translate them into actionable data solutions Stay up to date with advancements in sports analytics, machine learning, and data science methodologies Your Background 3+ years of industry experience as a Data Scientist , plus a strong academic foundation Python … Data Science Stack : Advanced proficiency in Python , including Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models ...