1 to 25 of 151 Machine Learning Jobs in the City of London

both their technical platform and engineering capability. The Role They are hiring a Senior Machine Learning Engineer to take ownership of their ML strategy and help scale the company’s modelling and data capabilities. This role sits at the core of the business - bridging research, engineering, and product … applied across their discovery pipeline. You’ll be expected to lead from the front: setting technical direction, mentoring engineers, and building production-grade ML systems that integrate closely with biological research workflows. What You’ll Be Doing Lead the design and development of machine learning models applied ...

Machine Learning Engineer (MLOps Python LLM AWS) London/WFH to £90k Are you a senior Machine Learning Engineer looking for an opportunity to take ownership, working on complex and interesting systems? You could be progressing your career in a hands-on technical leadership role … product challenges. You'll define the Machine Learning roadmap from a technical perspective, evaluating model architectures, optimising data pipelines and ensuring that ML features provide actionable insights within the product. This isn't just about training models; it's about building the ML infrastructure that enables the business ...

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 ...

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 ...

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 ...

parts of the supply chain—enabling bulk inventory exchange, streamlined digital payments, and generating vast quantities of aggregated market data. By applying AI and machine learning techniques, we deliver predictive market insight and trading recommendations straight back to the trading environment—facilitating smarter everyday business decisions … align data strategy with broader business goals. You will play an integral role, taking ownership of a team focused on designing, developing, and deploying ML systems to solve real world problems and enhance business processes, leveraging cloud platforms to build, train, and scale ML models. As Head of Data Science ...

Project Work with Hugging Face to build a sentiment analysis classifier using real-world AI techniques. Step 8 – AI Prompt Engineering Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems … Combine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine ...

Project Work with Hugging Face to build a sentiment analysis classifier using real-world AI techniques. Step 8 – AI Prompt Engineering Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems … Combine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine ...

engineering environment. Required: Core experience Strong grounding in audio machine learning or applied signal processing Experience training and evaluating models using modern ML tooling Awareness of real-world acoustic challenges such as noise, reverberation, and variability Comfort working in a small, fast-moving engineering team Likely … + some industry experience Beneficial Edge or embedded ML optimisation Audio feature extraction or DSP knowledge Postgraduate study in a relevant technical discipline Experience with sensing, monitoring, or real-world data systems ...

Title: Data Scientist/Machine Learning Engineer Location: UK Experience: 2–5 years Education: Master's in Science (IT/Computer Science/Engineer) Employment Type: Full-Time UK based candidates only. Information Tech Consultants Ltd (ITC) is an IT management and consulting firm based in Central London. … Services organizations as well as Mobile Apps and Service companies. Required Skills: 2-4 years of experience manipulating data sets and building statistical and machine learning models. Masters or Ph D in Statistics, Mathematics, Computer Science, or another quantitative field Fluent English (written/spoken) Strong hands ...

Machine Learning 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 … 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 ...

Machine Learning 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 ...

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 ...

Machine Learning Engineer (MLOps Python LLM AWS) London/WFH to £90k Are you a senior Machine Learning Engineer looking for an opportunity to take ownership, working on complex and interesting systems? You could be progressing your career in a hands-on technical leadership role ...

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. ...

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 ...

About the Company You will own the machine learning and computer vision components of the YEO CFR SDK. This means the facial recognition model, the three-layer passive liveness detection system (software depth analysis, pixelation detection, rPPG), anti-spoofing performance, and injection attack countermeasures. You will work alongside … pulse signal in variable lighting, at varying distances, on devices with no depth sensor, at frame rates as low as 15fps. Qualifications Tools & proficiency ML Frameworks: PyTorch (expert — primary training), TFLite (expert — Android/iOS inference), CoreML (strong — iOS Neural Engine), ONNX (strong — cross-platform interop), MediaPipe (competent — Android face ...

Innovation – London-Based Roles! Are you a tech trailblazer ready to build something extraordinary from the ground up? We’re looking for AI and Machine Learning Engineers with founding-level experience to join fast-growing start-ups in London and shape the future of technology. Why This Role … Exciting: Work with high-growth, innovative start-ups at the cutting edge of AI and machine learning Hands-on impact your work will directly influence products and company direction Collaborate with founding teams bring your ideas to life and help scale global solutions Hybrid flexibility: 3 days ...

deliver real products used by investment teams and portfolio companies. This role is ideal for: PhD graduates in a STEM field with applied ML, optimization, or computational experience; or Bachelor’s/Master’s graduates with 1–2 years of industry experience or relevant internships in machine learning, data engineering, or software engineering. You’ll contribute to designing, implementing, and deploying production-grade ML systems—ranging from NLP pipelines to model‐driven workflow automation. You’ll learn quickly, gain real ownership, and see your work make tangible business impact. What You’ll Do Build and deliver ML ...

creator ecosystem, and developing models for strategic content valuation. Multi-objective balancing and long term measurement. We rely extensively on state-of-the-art ML around personalization, deep learning, causal inference, optimization, ranking and recommendation. What You’ll Do Within the Sharechat AI team, we are looking … engagement while minimizing server and cloud costs of serving large scale models, and act as a subject matter expert in the recommender systems and ML ranking domains. You would be joining us at an exciting time! The science behind recommendation systems is rapidly changing, and we’re making big progress ...

experienced MLOps Engineer with a strong background in DevOps, Data Science, or Machine Learning Engineering, who has hands-on experience productionising ML models. The focus of the role is building and enabling production-grade ML environments rather than model development itself. Candidates must have deep MLflow experience … real-time) with robust CI/CD Work closely with Data Scientists to transition models from experimentation to production Build scalable, secure, and reproducible ML platforms Establish best practices around: Model governance; Monitoring and retraining; Environment management Integrate with cloud and data platforms such as Databricks, and potentially AWS SageMaker ...