1 to 25 of 92 TensorFlow Jobs in the UK excluding London

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

of tools and technologies across our products. For this role, we're looking for: Strong Python proficiency with hands-on experience in AI/ML frameworks including RAG, LangChain, TensorFlow, and PyTorch Practical experience with Generative AI and exposure to leading LLM platforms (Anthropic, Meta, Amazon , OpenAI) Proficiency with essential data science libraries including Pandas, NumPy, scikit-learn, Plotly More ❯

Stay up-to-date with the latest ML research and tools. Required Skills & Experience Strong proficiency in Python and its ML/data libraries (e.g., NumPy, pandas, scikit-learn, TensorFlow or PyTorch). Hands-on experience with Amazon SageMaker , including training, tuning, and deploying models. Solid understanding of machine learning algorithms and statistical methods. Experience with cloud platforms (AWS More ❯

and techniques in deep learning. Required Skills & Experience: Proven experience in developing and deploying deep learning models in production. Strong proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow, Keras). Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps More ❯

and techniques in deep learning. Required Skills & Experience: Proven experience in developing and deploying deep learning models in production. Strong proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow, Keras). Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps More ❯

and techniques in deep learning. Required Skills & Experience: Proven experience in developing and deploying deep learning models in production. Strong proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow, Keras). Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps More ❯

core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed data More ❯

from structured and unstructured data. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with More ❯

from structured and unstructured data. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with More ❯

from structured and unstructured data. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with More ❯

from structured and unstructured data. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with More ❯

Responsibilities: Research and deploy LLM-based solutions (e.g., LangChain, Mastra.ai, Pydantic) for document processing, summarization, and clinical Q&A systems. Develop and optimize predictive models using scikit-learn, PyTorch, TensorFlow, and XGBoost. Design robust data pipelines using tools like Spark and Kafka for real-time and batch processing. Manage ML lifecycle with tools such as Databricks , MLflow , and cloud More ❯

leadership to junior engineers, championing best practice and technical standards. What We're Looking For Strong experience across the ML lifecycle, including deploying models built in frameworks such as TensorFlow, PyTorch or Scikit-learn. Solid software engineering skills, with strong Python programming experience. Hands-on knowledge of cloud platforms (AWS, Azure, or GCP), containers, Docker, and Kubernetes. Understanding of More ❯

leadership to junior engineers, championing best practice and technical standards. What We're Looking For Strong experience across the ML lifecycle, including deploying models built in frameworks such as TensorFlow, PyTorch or Scikit-learn. Solid software engineering skills, with strong Python programming experience. Hands-on knowledge of cloud platforms (AWS, Azure, or GCP), containers, Docker, and Kubernetes. Understanding of More ❯

skills (Pandas, Numpy, Jupyter, FastAPI, SQLAlchemy). Expertise in AWS services (certification desirable). Proven experience deploying and supporting LLMs in production. Strong understanding of LLM fine-tuning (PyTorch, TensorFlow, Hugging Face Trainer, etc.). Experience with ML tooling (e.g. SageMaker, LangChain/LangSmith, MLflow, Dataiku, DataRobot). Knowledge of embeddings, their applications, and limitations. Hands-on experience in More ❯

Strong programming background; must be able to quickly become fluent in Python . Proficiency with key data science libraries (NumPy, Pandas, Scikit-Learn) and experience with deep learning frameworks (TensorFlow, PyTorch, or Caffe). Strong mathematical and statistical skills. Broad knowledge of core data science techniques, such as: Supervised/unsupervised machine learning Cross-validation and Bayesian inference Time More ❯

Active SC clearance(minimum 5 years UK residency required). Strong software engineering skills, with proficiency in Python. Experience across the ML lifecycle, including deploying models built in PyTorch, TensorFlow, or Scikit-learn. Hands-on cloud experience with AWS, Azure, or GCP. Knowledge of Docker and Kubernetes, with practical containerisation experience. Understanding of probability, statistics, and ML techniques. Strong More ❯

ML in production. What we're looking for Active SC Clearance Strong software engineering background with proficiency in Python. Experience deploying ML models built with frameworks such as PyTorch, TensorFlow, or Scikit-learn. Knowledge of probability, statistics, and common ML techniques. Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP). Proficiency with containers More ❯

that have tangible, high-profile impact. Essential experience: Strong background in Computer Vision and image/video processingHands-on experience with OpenCV pipelines Proficiency with deep learning frameworks (PyTorch, TensorFlow, Keras)Knowledge of CNN architectures and training dynamicsProven experience deploying and optimizing models for real-time applicationsStrong data science and metrics literacy Nice to have: PhD or Master's More ❯

or platform services using Microsoft and .NET . 4+ years in a Data Scientist or AI/ML Engineer role. Proficiency in Python , including libraries like pandas , scikit-learn , TensorFlow , and PyTorch . Deep understanding of ML algorithms : regression, classification, clustering, and deep learning. Hands-on experience with Large Language Models (LLMs) and techniques such as Retrieval-Augmented Generation More ❯

implementing solutions defined by project leads. Desirable Experience Strong Python expertise. Experience with GNU/Linux environments. Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face). Experience in natural language processing, tabular data analysis, or computer vision. SQL proficiency. Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures. Experience with CI More ❯

implementing solutions defined by project leads. Desirable Experience Strong Python expertise. Experience with GNU/Linux environments. Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face). Experience in natural language processing, tabular data analysis, or computer vision. SQL proficiency. Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures. Experience with CI More ❯

implementing solutions defined by project leads. Desirable Experience Strong Python expertise. Experience with GNU/Linux environments. Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face). Experience in natural language processing, tabular data analysis, or computer vision. SQL proficiency. Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures. Experience with CI More ❯