51 to 75 of 773 Remote/Hybrid Machine Learning Jobs

that supports machine learning workflow execution and deployment. Optimise data processing systems for throughput, scalability and efficiency. Collaborate closely with Data Engineers, ML Engineers and Research teams to deliver high-quality datasets and platform capabilities. Identify and resolve bottlenecks, performance issues and system failures. Contribute to engineering best … collaboration skills. Desirable Experience Experience working within large-scale data or platform engineering environments. Exposure to machine learning infrastructure, pipelines or production ML systems. Experience supporting data processing frameworks, orchestration platforms or workflow tooling. Familiarity with JVM-based distributed systems. Experience working within highly scalable cloud or distributed ...

then implementing them into scalable production software, rather than purely research-focused individuals. We are looking for someone with significant commercial experience in AI, machine learning or software engineering environments. You will work on a broad range of AI challenges spanning multimodal AI, machine learning, computer … from a top university is advantageous Achieved AAA at A Level or international equivalent Proven commercial experience in software engineering, AI engineering, machine learning or closely related areas Strong programming ability with confidence in quickly learning new technologies and languages. While the role currently uses Go, Kotlin ...

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 ...

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 ...

portfolio (formerly Onfido) powers document and biometric verification, fraud detection, and identity decisions at global scale. From detecting deepfakes to enabling seamless onboarding, our ML systems sit directly on the critical path of digital trust—used by banks, fintechs, marketplaces, and enterprises worldwide. About the Team: You’ll join … working at the intersection of machine learning, product, and real-world constraints. The team is supported by a state-of-the-art ML Ops ecosystem (AWS, Encord, Ray, PyTorch Lightning, Weights & Biases) and partners closely with product and engineering to ship models into production at scale. This ...

quarter. What You’ll Do? Design and help develop systems that serve recommendations to over 200 million users. Drive ML roadmap creation and execution, specifically around feed ranking and recall-oriented candidate generation systems. Provide technical guidance in ML model formulation, implementation, and experimentation, and take end-to-end ownership … ML systems and key user satisfaction-based metrics. Drive architectural strategy and design for complex ML systems that support the needs of users, creators, and content stakeholders. Who are you? 8+ years of professional experience in software engineering focused on deploying machine learning models in production at scale. ...

Data Scientist (ML Engineer) Location: Manchester Salary: £49,387 to £56,515 per annum Vacancy Type: Permanent, Full Time The closing date is 18 June 2026 Job summary Do you want to do meaningful work that makes a genuine difference to society? Our main purpose here at The National Institute … should we receive sufficient applications, so please don't delay your submission. Main duties of the job We're looking for a Data Scientist (ML Engineer) to help NICE apply machine learning and advanced analytics to real world health and care challenges. Working within the Architecture & Data team ...

Data Scientist (ML Engineer) Salary: £49,387 - £56,515 per annum Location: Manchester Vacancy Type: Permanent Closing date: 18/06/2026 The National Institute for Health and Care Excellence (NICE) is the independent organisation responsible for providing national guidance and advice on promoting high quality health, public health … should we receive sufficient applications, so please don’t delay your submission. Main duties of the job We’re looking for a Data Scientist (ML Engineer) to help NICE apply machine learning and advanced analytics to real world health and care challenges. Working within the Architecture & Data team ...

Title: Senior Machine Learning Engineer Location: Remote (UK) Salary: Up to £200,000 Plus Equity This is a unique opportunity for a Senior Machine Learning Engineer to join a high-performing quantitative trading firm building next-generation machine learning systems for financial markets. Working … models across a range of quantitative trading and research initiatives. Working closely with trading, data, and engineering teams, you will help build scalable ML systems capable of extracting signals from large and complex datasets while contributing to the long-term technical direction of the platform. Requirements A degree (preferably ...

Active SC required We're hiring an experienced SC-cleared Data Scientist to support a high-profile central government programme delivering advanced analytics and machine-learning solutions. This role is focused on turning complex datasets into predictive insight and deployable models that directly inform policy and operational decision … understanding of machine-learning techniques (supervised, unsupervised, model evaluation) Experience with MLOps practices, including: Model versioning and deployment CI/CD for ML workflows Monitoring and performance management Experience working with cloud-based data platforms ( AWS and/or Azure ) Strong statistical knowledge and ability to explain complex ...

should we receive sufficient applications, so please don't delay your submission. Main duties of the job We're looking for a Data Scientist (ML Engineer) to help NICE apply machine learning and advanced analytics to real world health and care challenges. Working within the Architecture & Data team … design, build and deploy robust machine learning solutions that support evidence based decision making, improve data practices, and enable the responsible use of AI across the organisation. You'll combine strong technical expertise with clear communication, ethical awareness and a collaborative mindset to turn complex data into meaningful ...

Data Scientist, London/Oxford hybrid working Are you excited by the opportunity to use machine learning, NLP, and generative AI to help researchers discover knowledge faster and make better decisions? Would you enjoy turning complex scientific and business challenges into practical, production-ready AI solutions that create … strive to maintain. Our team values trust, respect, collaboration, agility, and quality. About the Role In this role, you will design and build machine learning, NLP, and generative AI solutions that support scientific discovery, knowledge extraction, decision support, and intelligent content understanding. You will work with large-scale ...

role of patents in providing a window into who is leading technology innovation, where, and at what pace. By harnessing the latest advances in machine learning combined with expert analysis, LexisNexis Intellectual Property is transforming how actionable insights are extracted from patent data. Information can now be accessed … aspects of the delivery process - from scoping support through to project delivery. The primary responsibility of this role will be the training of machine learning classifiers using patent data across a variety of technologies and industries. This supervised learning process is conducted using the proprietary LexisNexis Classification ...