1 to 25 of 132 Permanent TensorFlow Jobs in the UK

University with a strong quantitative curriculum is highly valued. Requirements 8+ years of experience building, training, and evaluating Deep Learning and Machine Learning models using tools such as PyTorch , TensorFlow , scikit learn , HuggingFace , or LangChain . Experience in a start up or a cross functional team is a plus Experience in Natural Language Processing (NLP) is a plus Strong More ❯

science lifecycle. Expertise taking projects from ideation or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model drift detection, decay, A/B testing, integration testing, differential testing, Python package building, and code version control. Skilled More ❯

science lifecycle. Expertise taking projects from ideation or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model drift detection, decay, A/B testing, integration testing, differential testing, Python package building, and code version control. Skilled More ❯

related field. 7+ years of professional software development experience, with at least 3 years in AI/ML. Strong proficiency in Python , including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch . Solid understanding of ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on More ❯

with predictive modeling techniques such as regression, classification, clustering, or time-series forecasting. Proficiency in Python and experience with data science libraries (e.g., Pandas, NumPy, scikit-learn, XGBoost, PyTorch, TensorFlow). Strong experience with SQL and data manipulation across large datasets. Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Plotly, Tableau, or Power BI). Exposure to modern collaborative More ❯

Azure , AWS , or GCP . Strong understanding of data lakehouse architectures, CI/CD, and containerisation (Docker/Kubernetes). Proficiency with modern ML and AI frameworks including PyTorch , TensorFlow , LangChain , and enterprise AI platforms. Practical experience with LLMs, embeddings, RAG, and AI agent/chatbot development. Ability to write production-grade Python code and build APIs. Strong communication More ❯

science. Requirements 5+ years of experience in data science, machine learning, or AI model development. Expertise in Python, R, or Julia, with proficiency in pandas, NumPy, SciPy, scikit-learn, TensorFlow, or PyTorch. Experience with SQL, NoSQL, and big data technologies (Spark, Hadoop, Snowflake, Databricks, etc.). Strong background in statistical modelling, probability theory, and mathematical optimization. Experience deploying machine More ❯

or platform scale. Proven track record of leading ML engineering projects from architecture to deployment, including ownership of production-grade systems. Deep expertise in ML frameworks and engineering stacks (TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow). Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++). Strong understanding of cloud ML infrastructure (AWS SageMaker More ❯

emerging GenAI applications. The work you could be doing • Design and deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. • Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery and monitoring. • Apply advanced More ❯

integrations, asyncio for concurrent 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, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning More ❯

integrations, asyncio for concurrent 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, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning More ❯

depth knowledge and familiarity with cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Technical Skills – Good to have: • Expertise in any one framework (TensorFlow, Pytorch, Keras) • Experience in a statistical programming language (e.g. R or Python) and applied machine learning and AI techniques (i.e computer vision, deep learning, conversational AI, and natural language More ❯

in AI, machine learning, and data science methodologies. Experienced Needed: Masters or PhD in a STEM subject Proficiency in Python, with experience in libraries such as pandas, scikit-learn, TensorFlow, or PyTorch. Solid SQL skills and experience working with relational databases. Exposure to cloud platforms (AWS, GCP, or Azure) would be advantageous. Strong analytical and problem-solving abilities, with More ❯

technical field (Statistics, Mathematics, Physics, Computer Science, Machine Learning) Strong programming skills in Python (production-level) and SQL; confident with modern ML/AI libraries such as scikit-learn, TensorFlow, or PyTorch Familiarity with MLOps frameworks, model deployment, and cloud-based platforms (Databricks, AWS, Azure) Strong experience with data visualisation tools and techniques; able to turn complex results into More ❯

consistent track record of shipping models to production and supporting them post-deployment. Strong Python programming skills, including object-oriented design and proficiency with key ML libraries (e.g., PyTorch, TensorFlow, Scikit-Learn). Solid understanding of probability and statistical modeling to support robust model development and interpretation. Experience with cloud platforms (especially Azure and/or AWS) and modern More ❯

RFI/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools such as Terraform . Strong background in More ❯

ML Skills: Proficiency in Python, with a deep understanding of machine learning algorithms, deep learning, and generative models. Advanced AI Skills and Testing: Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch), hands-on experience in deploying AI/ML solutions as a service/REST API on Cloud or Kubernetes, and proficiency in testing of developed AI components. Responsibilities 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 ❯

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 ❯

container orchestration (Kubernetes, Docker). Expertise in CI/CD pipelines, infrastructure as code (Terraform, Ansible), and automated monitoring. Proficiency in ML frameworks and programming languages such as Python, TensorFlow, PyTorch, or similar. Experience with API design, microservices architecture, and system integration. Strong leadership, project management, and communication skills, with the ability to influence cross-functional teams. Familiarity with More ❯

excellent understanding of key concepts in computer science (e.g. databases, software engineering practices, cloud computing - especially AWS) and data science (e.g. machine learning process) Excellent knowledge of Python includingPytorch, Tensorflow andSKLearn as well as initial knowledge of LangChain andRAGAS. Familiarity with CI/CD workflows is required and experience with containerisation and deployment using Docker/Kubernetes will be More ❯

techniques and how to fine tune those models - e.g., XGBoost, Deep Neural Networks, Transformers, ResNets, VAEs, GANs, Markov chains, etc. Experience using specialized machine learning libraries - e.g., Fastai, Keras, Tensorflow, pytorch, sci kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. Experience of using More ❯

understanding of machine learning algorithms, optimization, architectures, and evaluation. Experience with data engineering workflows, including ETL, pipeline design, and feature extraction. Proficiency in Python and libraries such as PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, and OpenAI/Anthropic APIs. Familiarity with DevOps & tooling (Git, CI/CD, API integration), front-end & visualization (Streamlit, Gradio, React), and cloud environments More ❯

concept , model monitoring , and adoption of emerging AI tech. What We’re Looking For 5+ years in data engineering, with team or project leadership experience. Advanced Python (Pandas, PyTorch, TensorFlow, Scikit-learn). Strong AWS/GCP , MySQL , and CI/CD experience; Docker/Kubernetes a plus. Excellent communication, organisation, and problem-solving skills. Passion for innovation and More ❯

learning, including model architectures, training, and evaluation. Proven research experience in audio/speech processing with publications in top-tier conferences/journals. Proficiency with ML frameworks such as TensorFlow or PyTorch. Strong analytical and problem-solving skills; ability to design and execute rigorous experiments. Excellent communication and teamwork skills. Desirable skills: Experience in audio signal processing (e.G., Filter More ❯