26 to 50 of 3,056 PyTorch Jobs in the UK

classification problems Data Engineering: Experience with data pipeline development, ETL processes, and working with large datasets Hands on experience with ML tools like TensorFlow, PyTorch etc. Experience with Azure cloud (AKS, Azure AI, ADF, Document Intelligence etc.) Excellent problem-solving skills and software engineering practices Excellent communication skills Preferred Qualifications More ❯

algorithms. Proficiency in Python and its standard coding practices and common libraries. Experience with ML models and knowledge of at least one ML framework (PyTorch preferred). Experience in data preprocessing techniques, feature engineering, and model evaluation metrics. Proven ability to manipulate, query and visualise data and training/evaluation More ❯

and related frameworks (RAG apps, vector DBs, LangChain, LlamaIndex, agentic frameworks, ...) Advanced knowledge of Python and machine learning frameworks (SciPy, Scikit-learn, TensorFlow, PyTorch, pyMC, pgmpy, ...) Hands-on experience with one or more cloud computing platforms (Azure - preferred, AWS, GCP). Understanding of the whole ML lifecycle and More ❯

with CAD or 3D modelling software (e.g., Blender, 3DS Max). Core Skills: Machine Learning, Deep Learning, Python, Docker, Data Warehousing Other Skills: Tensorflow, PyTorch, AWS Seniority: Senior, Lead More ❯

Stay informed on advancements in AI and integrate the latest technologies into solutions. Key Skills & Experience: Strong programming skills in Python (experience with TensorFlow, PyTorch, or similar frameworks). Expertise in deploying models on cloud platforms (Azure) or containerised environments (Docker, Kubernetes). Proficiency in machine learning algorithms and advanced More ❯

Science, Artificial Intelligence, Data Science, or a related field (or equivalent experience). Technical Skills: Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch). Strong understanding of NLP techniques, including tokenization, embeddings, transformers, and attention mechanisms. Experience in retraining and fine-tuning LLMs using large-scale datasets. Familiarity More ❯

EC2, S3, Athena, Glue). Strong problem-solving and teamwork skills. Leadership and mentorship abilities. Highly Desirable Skills Experience with deep learning libraries (Keras, PyTorch, TensorFlow). Knowledge of data processing tools (Pyspark, Hive) and databases (MongoDB, SQL, NoSQL). NLP expertise (nltk, SpaCy), especially with unstructured financial data. Understanding More ❯

adapt to new tools and technologies. Preferred Qualifications (Nice-to-Have): Basic understanding of machine learning concepts and frameworks like Scikit-learn, TensorFlow, or PyTorch . Understanding of DevOps practices and CI/CD pipelines. Familiarity with cloud services such as AWS, Azure, or GCP. Hands-on experience with AI More ❯

adapt to new tools and technologies. Preferred Qualifications (Nice-to-Have) Basic understanding of machine learning concepts and frameworks like Scikit-learn, TensorFlow, or PyTorch . Understanding of DevOps practices and CI/CD pipelines. Familiarity with cloud services such as AWS, Azure, or GCP. Hands-on experience with AI More ❯

like GANs, VAEs, transformers, or diffusion models. Proficiency in programming languages such as Python, R, or Julia, and experience with ML frameworks like TensorFlow, PyTorch, or Keras. Strong understanding of deep learning architectures, neural networks, and statistical modeling techniques. Experience with cloud platforms (AWS, Azure, Google Cloud) and MLops tools More ❯

science. • Strong understanding of AI/ML algorithms and techniques, including LLMs, GenAI, and automated AI systems. • Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data science libraries (e.g., NumPy, pandas, scikit-learn). • Proficiency in Python, R, or other relevant programming languages. • Proficiency in working with large datasets More ❯

experience in AI development. Strong understanding of machine learning, deep learning, and other AI techniques. Proficiency in Python and relevant AI libraries (e.g., TensorFlow, PyTorch). Experience with data processing and analysis tools (e.g., Pandas, SQL). Experience with cloud platforms such as Amazon Web Services, Azure, or Google Cloud More ❯

Scikit-learn etc.). • Solid understanding of core machine learning concepts • Hands-on experience with at least one major deep learning framework (e.g., TensorFlow, PyTorch, Keras). • Proven understanding of software engineering principles, data structures, algorithms, and design patterns. • Experience with version control systems (e.g., Git). • Excellent problem-solving More ❯

particularly in fraud prevention and user personalization. Experience designing, developing, and implementing advanced machine learning models. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Data Engineering Skills: Proficiency in developing and maintaining real-time data pipelines for processing large-scale data. Experience with ETL processes for More ❯

At least 2 years' experience with machine learning, deep learning, or AI systems. Familiarity with key libraries and frameworks such as TensorFlow, Scikit-learn, PyTorch, Transformers, LangChain, BeautifulSoup, and OpenCV. Experience with AI APIs and platforms (e.g., OpenAI, Bedrock, Gemini, Anthropic). Knowledge of cloud infrastructure (ideally AWS), Kubernetes, and More ❯

experience in AI development. Strong understanding of machine learning, deep learning, and other AI techniques. Proficiency in Python and relevant AI libraries (e.g., TensorFlow, PyTorch). Experience with data processing and analysis tools (e.g., Pandas, SQL). Experienced with cloud platforms such as Amazon Web Services, Azure, or Google Cloud More ❯

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., PyTorch, MLFlow, JAX) Distributed computing frameworks (e.g., Spark, Dask) Cloud platforms (e.g., AWS, Azure, GCP) and HP computing Containerization and orchestration (Docker, Kubernetes) Ability to scope More ❯

experience in AI development. Strong understanding of machine learning, deep learning, and other AI techniques. Proficiency in Python and relevant AI libraries (e.g., TensorFlow, PyTorch). Experience with data processing and analysis tools (e.g., Pandas, SQL). Experienced with cloud platforms such as Amazon Web Services, Azure, or Google Cloud More ❯

organization is considered an asset. Other skills we are searching for are: Programming Skills: Proficiency in Python, data analytics, deep learning (Scikit-learn, Pandas, PyTorch, Jupyter, pipelines), and practical knowledge of data tools like Databricks, Ray, Vector Databases, Kubernetes, and workflow scheduling tools such as Apache Airflow, Dagster, and Astronomer. More ❯

ML algorithms—ranging from classical techniques to deep learning methods. Proven experience in training, fine-tuning, and deploying neural network models using frameworks like PyTorch, with a focus on optimizing performance and scalability for realworld applications. Demonstrated ability to mentor and lead technical teams, influence technical strategy, and drive innovation More ❯

Experience Academic Background: Advanced degree in AI, statistics, mathematics, computer science, or a related field. Programming and Frameworks: Deep experience with Python, TensorFlow or PyTorch, and NLP libraries such as spaCy and Hugging Face. GenAI Tools: Practical experience with LangChain, Hugging Face Transformers, and embedding models for building GenAI applications. More ❯

strong proficiency in writing scale and production-grade code in Python. Solid understanding and proficiency in working with Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch, SQL. Proficiency in advanced data visualization tools and libraries (e.g., Matplotlib, Seaborn, Plotly, Tableau) for creating insightful and interactive visualizations. Solid understanding of data structures More ❯

strong proficiency in writing scale and production-grade code in Python. Solid understanding and proficiency in working with Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch, SQL. Proficiency in advanced data visualization tools and libraries (e.g., Matplotlib, Seaborn, Plotly, Tableau) for creating insightful and interactive visualizations. Solid understanding of data structures More ❯

strong proficiency in writing scale and production-grade code in Python. Solid understanding and proficiency in working with Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch, SQL. Proficiency in advanced data visualization tools and libraries (e.g., Matplotlib, Seaborn, Plotly, Tableau) for creating insightful and interactive visualizations. Solid understanding of data structures More ❯

strong proficiency in writing scale and production-grade code in Python. Solid understanding and proficiency in working with Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch, SQL. Proficiency in advanced data visualization tools and libraries (e.g., Matplotlib, Seaborn, Plotly, Tableau) for creating insightful and interactive visualizations. Solid understanding of data structures More ❯