1 to 25 of 108 TensorFlow Jobs in the UK excluding London

algorithms and NLP techniques, with hands-on experience in either academia or industry. Proficiency in Python and experience with ML/NLP libraries like TensorFlow, PyTorch, scikit-learn, or Hugging Face. Experience working with cloud platforms such as AWS, GCP, or Azure. Familiarity with advanced NLP methods, including Prompt More ❯

related field. Proficiency in programming languages such as Python, R, or Java. Strong understanding of mathematics and statistics. Experience with machine learning frameworks like TensorFlow, Keras, or PyTorch. Knowledge of data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib). Familiarity with big data technologies (e.g., Hadoop, Spark). More ❯

how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, 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 More ❯

and developing custom solutions using large language models (LLMs) for business use cases. Proficient in Python-based AI/ML development using frameworks like TensorFlow, PyTorch, and Scikit-learn. LLM Orchestration and Development Expertise in building LLM-powered applications using frameworks such as LangChain and LangGraph, including prompt engineering More ❯

and developing custom solutions using large language models (LLMs) for business use cases. Proficient in Python-based AI/ML development using frameworks like TensorFlow, PyTorch, and Scikit-learn. LLM Orchestration and Development Expertise in building LLM-powered applications using frameworks such as LangChain and LangGraph, including prompt engineering More ❯

years of hands-on experience in AI and machine learning. Technical Expertise: Strong proficiency in Python. Expertise in machine learning frameworks such as TensorFlow, PyTorch, Keras, or similar. Machine Learning: Solid understanding of supervised and unsupervised learning, deep learning (CNNs, RNNs, GANs), and reinforcement learning. Data Handling: Proficient in More ❯

in computer science, Software Engineering, or a related field. Proficiency in programming languages (e.g., Python, C#, C++). Experience with deep learning frameworks (e.g., TensorFlow, PyTorch). Knowledge of cloud platforms (e.g., Azure). Familiarity with containerization is a plus (e.g., Docker, Kubernetes). Knowledge of big data technologies More ❯

for you if: Python for API and Model development (Machine learning frameworks and tooling e.g. Sklearn) and (Deep learning frameworks such as Pytorch and Tensorflow). Understanding of machine learning techniques. Experience with data manipulation libraries (e.g. Pandas, Spark, SQL). Git for version control. Cloud experience (we use More ❯

for you if: Python for API and Model development (Machine learning frameworks and tooling e.g. Sklearn) and (Deep learning frameworks such as Pytorch and Tensorflow). Understanding of machine learning techniques. Experience with data manipulation libraries (e.g. Pandas, Spark, SQL). Git for version control. Cloud experience (we use More ❯

for you if: Python for API and Model development (Machine learning frameworks and tooling e.g. Sklearn) and (Deep learning frameworks such as Pytorch and Tensorflow). Understanding of machine learning techniques. Experience with data manipulation libraries (e.g. Pandas, Spark, SQL). Git for version control. Cloud experience (we use More ❯

in Machine Learning, Deep Learning, and NLP Experience with transformer models (BERT, GPT, LLaMA, etc.) Proficiency in SQL, Python and ML libraries such as TensorFlow, PyTorch, Hugging Face, and Scikit-Learn Expertise in personalisation techniques (recommendation systems, user segmentation, dynamic pricing) Experience working with sensitive and business critical data More ❯

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

of power system fundamentals, including power flow, optimal power flow (OPF), and common operational challenges. Experience with machine learning models using frameworks, such as TensorFlow or Jax. Proficiency in Python Experience using commercial or open-source optimization solvers, such as Gurobi, CPLEX, IPOPT, or similar. A strong interest in More ❯

performance. Requirements: Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Bioinformatics, or related field. Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch). Experience with bioinformatics tools (e.g., Biopython, RDKit). Strong knowledge of statistical models, deep learning, and data preprocessing. Familiarity with cloud platforms More ❯

5+ years of experience in ML engineering, with at least 2 years in a leadership role. Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Strong experience with cloud platforms (AWS, GCP, or Azure) and containerisation (Docker, Kubernetes). Experience gained in Tech for Good More ❯

deep learning, probabilistic modeling, and/or NLP. Proficiency in programming languages such as Python, R, or Julia, with experience using ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Familiarity with biological datasets such as genomic, transcriptomic, proteomic, or imaging data. Experience working with large-scale datasets and cloud More ❯

you will bring: Essential: 5+ years of experience in data science, machine learning, or AI innovation. Strong coding skills in Python (e.g., Pandas, NumPy, TensorFlow, PyTorch) and SQL. Proven experience deploying ML models in production environments. 3+ years in a leadership or mentorship role within AI or data teams. More ❯

S3, Lambda, SageMaker, ECS) and cloud infrastructure management Programming and ML Frameworks: Proficiency in Python and experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch CI/CD Experience: Experience with CI/CD tools and practices (e.g., GitHub Actions, Jenkins) and the ability to design and More ❯

across the Civil Service. Key Skills and Experience • Strong programming skills in Python, R, or SQL. • Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch). • Proficiency in data manipulation and analysis (e.g., Pandas, NumPy, Spark). • Knowledge of data visualization tools (e.g., Power BI, Tableau, Matplotlib). More ❯

programming skills in Python or R, with proficiency in data manipulation libraries (e.g., Pandas, NumPy). Experience with machine learning frameworks (e.g., Scikit-Learn, TensorFlow) and data visualization tools (e.g., Tableau, Matplotlib). Solid understanding of statistics, probability, and data-driven decision-making. Experience working with databases (SQL) and More ❯

in machine learning/NLP roles, with recent focus on LLMs and/or GenAI. Strong proficiency in Python, deep learning frameworks (PyTorch or TensorFlow), and GenAI libraries (LangChain, LlamaIndex, Transformers). Hands-on experience with vector search, embedding models, and retrieval pipelines. Familiarity with prompt engineering, prompt tuning More ❯

. Stay up-to-date with the latest research and advancements in AI, Image processing, and Computer Vision. Familiarity with deep learning frameworks (PyTorch, TensorFlow) including OpenCV. Strong communication and analytical skills. Ability to work in teams in a diverse, multi-stakeholder environment comprising of Business and Technology teams. More ❯

CI/CD pipelines, monitoring frameworks). Strong proficiency in Python and libraries like Pandas, NumPy, and scikit-learn, with familiarity in frameworks like TensorFlow or PyTorch being a plus Cloud experience—AWS preferred - including deploying and managing models in cloud environments (will still consider GCP/Azure) Curious More ❯

CI/CD pipelines, monitoring frameworks). Strong proficiency in Python and libraries like Pandas, NumPy, and scikit-learn, with familiarity in frameworks like TensorFlow or PyTorch being a plus Cloud experience—AWS preferred - including deploying and managing models in cloud environments (will still consider GCP/Azure) Curious More ❯

foundation in both machine learning and software engineering. Experience deploying machine learning models in production environments. Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Familiarity with cloud platforms like GCP, AWS, or Azure. Experience with CI/CD pipelines for machine learning (e.g., Vertex More ❯