51 to 75 of 202 Remote TensorFlow Jobs in England

you also had experience in some of these, but if not we’ll help you with them Machine Learning software e.g. Amazon and Azure machine learning, or Google's TensorFlow Deploying and managing in Cloud Computing such as AWS, MS Azure Containerisation e.g. Docker, Kubernetes Relational, document, or graph database systems Software configuration and deployment e.g. Ansible Security Clearance More ❯

you also had experience in some of these, but if not we’ll help you with them Machine Learning software e.g. Amazon and Azure machine learning, or Google's TensorFlow Deploying and managing in Cloud Computing such as AWS, MS Azure Containerisation e.g. Docker, Kubernetes Relational, document, or graph database systems Software configuration and deployment e.g. Ansible Security Clearance More ❯

you also had experience in some of these, but if not we’ll help you with them Machine Learning software e.g. Amazon and Azure machine learning, or Google's TensorFlow Deploying and managing in Cloud Computing such as AWS, MS Azure Containerisation e.g. Docker, Kubernetes Relational, document, or graph database systems Software configuration and deployment e.g. Ansible How we More ❯

see from you: Strong understanding of a wide range of ML algorithms. Understanding of MLOps practices for managing and monitoring models in production. Experience with deep learning frameworks (e.g. TensorFlow, PyTorch). Proficiency in programming languages such as Python, SQL and R. Knowledge of SQL and NoSQL databases for data storage and retrieval. Experience with LLM application design and More ❯

see from you: Strong understanding of a wide range of ML algorithms Understanding of MLOps practices for managing and monitoring models in production. Experience with deep learning frameworks (e.g. TensorFlow, PyTorch) Proficiency in programming languages such as Python, SQL and R. Knowledge of SQL and NoSQL databases for data storage and retrieval. Strong software engineering skills, including version control More ❯

Any candidates with exposure to computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps, CI/CD for ML, and model performance monitoring. More ❯

monitoring frameworks Participate in code reviews, design discussions, and performance tuning Requirements: 3+ years of experience in a Machine Learning Engineer or similar role Proficiency in Python , ML frameworks (TensorFlow, PyTorch), and deployment tools (Docker, MLflow, etc.) Experience building scalable ML pipelines in cloud environments (AWS, GCP or Azure) Familiarity with energy systems, smart metering, or IoT data is More ❯

software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps) Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow) Distributed computing frameworks (e.g., Spark, Dask) Cloud platforms (e.g., AWS, Azure, GCP) and HP computing Containerization and orchestration (Docker, Kubernetes) Strong problem-solving skills and the ability to More ❯

monitoring frameworks Participate in code reviews, design discussions, and performance tuning Requirements: 3+ years of experience in a Machine Learning Engineer or similar role Proficiency in Python , ML frameworks (TensorFlow, PyTorch), and deployment tools (Docker, MLflow, etc.) Experience building scalable ML pipelines in cloud environments (AWS, GCP or Azure) Familiarity with energy systems, smart metering, or IoT data is More ❯

monitoring frameworks Participate in code reviews, design discussions, and performance tuning Requirements: 3+ years of experience in a Machine Learning Engineer or similar role Proficiency in Python , ML frameworks (TensorFlow, PyTorch), and deployment tools (Docker, MLflow, etc.) Experience building scalable ML pipelines in cloud environments (AWS, GCP or Azure) Familiarity with energy systems, smart metering, or IoT data is More ❯

Data Scientist 's working across a variety of companies & industries Provide hands-on coding sessions and real-world project mentorship using Python and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch etc) Incorporate cloud-based tools and services (e.g., AWS, GCP, or Azure) into training to simulate modern ML engineering environments (Cloud Training can be provided to you) Keep More ❯

Data Scientist 's working across a variety of companies & industries Provide hands-on coding sessions and real-world project mentorship using Python and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch etc) Incorporate cloud-based tools and services (e.g., AWS, GCP, or Azure) into training to simulate modern ML engineering environments (Cloud Training can be provided to you) Keep More ❯

machine learning or engineering roles. Strong expertise in Machine Learning and Deep Learning, with exposure to Reinforcement Learning as a plus. Proficiency in Python and modern ML libraries (e.g., TensorFlow, PyTorch, JAX, or Keras). Experience with version control systems (GitHub, GitLab) and knowledge of clean, maintainable coding practices. Familiarity with CI/CD pipelines for automating ML workflows. More ❯

Domain experience in finance, insurance, or e-commerce is a plus but not required. - Experience deploying models in cloud environments. - Familiarity with machine learning frameworks and libraries such as TensorFlow, CatBoost, XGBoost, Scikit-learn, and Pandas. - Experience developing and integrating APIs. - Proficiency in SQL. - Background in software engineering and exposure to DevOps/MLOps practices. - Working knowledge of CI More ❯

Domain experience in finance, insurance, or e-commerce is a plus but not required. - Experience deploying models in cloud environments. - Familiarity with machine learning frameworks and libraries such as TensorFlow, CatBoost, XGBoost, Scikit-learn, and Pandas. - Experience developing and integrating APIs. - Proficiency in SQL. - Background in software engineering and exposure to DevOps/MLOps practices. - Working knowledge of CI More ❯

Strong understanding of data structures, data modeling, and software architecture Data Science Library Knowledge: Deep understanding of key Data Science and Machine Learning libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow), with a preference for PySpark experience Model Productionisation: Experience in taking Machine Learning models from development to production CI/CD and MLOps Experience : Familiarity with Continuous Integration and More ❯

best practices, including model versioning, CI/CD pipelines, containerization, and cloud deployment for large-scale models. Solid programming skills in Python and familiarity with machine learning frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and MLOps tools (e.g., MLflow, Kubeflow). Strong analytical and problem-solving skills, with an aptitude for translating complex theoretical research into practical applications. Day More ❯

or related field • 3+ years' experience in applied R&D or research consultancy (public sector/defence background advantageous) • Strong Python skills and experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Expertise in one or more of the following areas : o Multi-modal data fusion or time series analysis o Uncertainty quantification or probabilistic modelling o Active learning More ❯

s degree (or equivalent) in Computer Science, Machine Learning, or a related field. 3+ years of experience deploying ML models in production. Proficient in Python and ML frameworks (e.g., TensorFlow, PyTorch). Experience working with cloud platforms and containerised deployments (e.g., Docker, Kubernetes). Solid grounding in computer vision and experience with large-scale data. Bonus: exposure to reinforcement More ❯

Prior experience in anomaly detection applied to areas such as QA, gameplay analysis, computer vision or video stream analysis. Proven experience with machine learning frameworks such as Python, NumPy, TensorFlow, or PyTorch. Strong understanding of data analysis, signal processing, and statistical modeling. Experience with video processing and understanding of game streaming technologies is a plus. Problem-Solving Skills: Excellent More ❯

Prior experience in anomaly detection applied to areas such as QA, gameplay analysis, computer vision or video stream analysis. Proven experience with machine learning frameworks such as Python, NumPy, TensorFlow, or PyTorch. Strong understanding of data analysis, signal processing, and statistical modeling. Experience with video processing and understanding of game streaming technologies is a plus. Problem-Solving Skills: Excellent More ❯

Desirable Familiarity with cloud data platforms (Snowflake, Redshift, BigQuery) and ETL tools (dbt, Airflow) Experience with loyalty-programme analytics or CRM platforms Knowledge of machine-learning frameworks (scikit-learn, TensorFlow) for customer scoring Technical Toolbox Data & modeling: SQL, Python/R, pandas, scikit-learn Dashboarding: Tableau or Power BI ETL & warehousing: dbt, Airflow, Snowflake/Redshift/BigQuery Experimentation More ❯

experience in applied machine learning, research engineering, or software development. Strong foundation in ML, deep learning, and preferably reinforcement learning. Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Keras, or JAX. Experience with version control (GitHub, GitLab), clean code practices, and CI/CD workflows. Comfort working in a fast-paced, collaborative, and research-driven environment. Nice More ❯

experience in applied machine learning, research engineering, or software development. Strong foundation in ML, deep learning, and preferably reinforcement learning. Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Keras, or JAX. Experience with version control (GitHub, GitLab), clean code practices, and CI/CD workflows. Comfort working in a fast-paced, collaborative, and research-driven environment. Nice More ❯

heavy or AI environments. Proficiency in Python, R, C/C++, or Java, and awareness of security risks associated with these languages. Familiarity with key ML frameworks such as TensorFlow, PyTorch, JAX, and scikit-learn. Strong grasp of DevOps/CI/CD workflows and secure SDLC methodologies. Experience securing cloud-native environments, including containerized services (e.g., Kubernetes) and More ❯