876 to 900 of 1,810 PyTorch Jobs in the UK

4+ years managing technical teams (ML engineers, data scientists, or similar) Expert knowledge of ML fundamentals: supervised/unsupervised learning, time series, causal inference Experience with modern ML frameworks (PyTorch, TensorFlow, scikit-learn, CatBoost) Growth & Product Experience Experience with growth analytics, attribution modeling, or marketing effectiveness Understanding of user acquisition funnels and retention optimization Comfortable translating business requirements into technical More ❯

Profile Essential skills: Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field. Proficiency in deep learning frameworks like PyTorch/JAX. Strong Python software development skills (nice to have C other languages). Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling. Capable of designing, executing and reporting More ❯

and business audiences. Strong discovery and pain point identification skills, adept at uncovering customer needs Proficiency in coding, preferably with Python Experience with common data science libraries, such as PyTorch, TensorFlow, and scikit-learn Familiarity with distributed computing frameworks, including Spark, Ray, and Dask. Comfortable working in Linux environments, with hands-on experience in system navigation and troubleshooting Understanding of More ❯

rendering techniques and/or image-to-image translation MSC/Ph.D. in computer science, electrical engineering or equivalent Strong programming skills in Python and C++ and Experience with PyTorch or similar machine learning frameworks EPIC JOB + EPIC BENEFITS = EPIC LIFE We pay 100% for benefits except for PMI (for dependents). Our current benefits package includes pension, private More ❯

related field (Master’s preferred). 1+ year(s) of experience in AI, machine learning, or data science. Proficiency in Python and experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn . Strong understanding of data analysis , feature engineering , and model evaluation techniques, particularly with retail and consumer-facing data. Passion for AI and the retail industry, with More ❯

Qualifications Proven track record of developing and deploying LLM-based solutions in an enterprise setting as a senior/staff scientist. Proficiency in Python and libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and spaCy. Strong understanding of LLM architectures (e.g., GPT, BERT, T5) and experience fine-tuning them for specific tasks. Demonstrable experience in utilising different model architectures and More ❯

a rough guide): Understanding of and interest in the full machine learning lifecycle, including deploying trained machine learning models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch; Understanding of the core concepts of probability and statistics and familiarity with common supervised and unsupervised learning techniques; Experience in Software Engineering including programming in Python; Technical experience of cloud More ❯

presentations and communicate complex findings to technical and non-technical stakeholders• Identify and explore new data sources and technologies to drive innovation Qualifications • Strong experience with Python, SQL, TensorFlow, PyTorch, GCP, Docker, and LLM technologies• Ability to solve complex, ambiguous problems with minimal guidance• Excellent communication skills for explaining technical concepts to non-technical audiences• Experience in team leadership and More ❯

presentations and communicate complex findings to technical and non-technical stakeholders• Identify and explore new data sources and technologies to drive innovation Qualifications • Strong experience with Python, SQL, TensorFlow, PyTorch, GCP, Docker, and LLM technologies• Ability to solve complex, ambiguous problems with minimal guidance• Excellent communication skills for explaining technical concepts to non-technical audiences• Experience in team leadership and More ❯

Experience with cloud AI services, model deployment, monitoring, and CI/CD pipelines for ML models (MLOps best practices). Example Tools & Technologies: Frameworks & Libraries: LangChain, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn Agentic AI Tools: OpenAI GPT models, Crew,AI, Cohere, Pinecone (for vector databases), AutoGPT Data Engineering & ML Pipelines: Apache Airflow, MLflow, Kubeflow, dbt, Prefect Cloud & Deployment Platforms More ❯

or similarly strong academic background in machine learning, with 2+ years of hands-on experience in with LLM fine-tuning, RAG systems, and prompt engineering. Proficiency with frameworks like PyTorch, Tensorflow, JAX, Pandas, and OpenCV. A solid understanding of transformer models and their common variants, loss functions, and pitfalls. A quick learner with a structured, organized approach to problem-solving. More ❯

and clients, and translate complex technical concepts into business value and actionable insights. Proficiency in programming languages and tools for data analysis, such as Python, R, SQL, Spark, TensorFlow, PyTorch, etc. Experience in working with large and complex datasets, both structured and unstructured, and applying advanced techniques such as deep learning, natural language processing, computer vision, or reinforcement learning. Expert More ❯

data from a human expert perspective to identify errors and inconsistencies. You have 7+ years of programming experience in Python and have development experience with both DL toolkits like PyTorch or Tensorflow and can deploy models with clean APIs. You are equally capable as a software engineer as you are in formulating novel research ideas and your code proves it. More ❯

in cybersecurity, with a strong emphasis on threat detection. Solid experience in machine learning and data science, proficient in both supervised and unsupervised algorithms and frameworks such as TensorFlow, PyTorch, or Scikit-learn. Experience working with large, real-world datasets that require pre-processing, data visualization, and a great dose of analytical skills. Preferred requirements: Undergraduate or postgraduate in Computer More ❯

one or more Data Science or AI domains Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Cloud platforms (AWS/Azure/GCP) Experience with AI governance and responsible AI practices Understanding of More ❯

one or more Data Science or AI domains Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Cloud platforms (AWS/Azure/GCP) Experience with AI governance and responsible AI practices Understanding of More ❯

compiled languages like Go or Rust. At least a B.Sc. equivalent in a Science, Technology, Engineering or Mathematical discipline. Knowledge of AI/ML frameworks such as TensorFlow and PyTorch, as well as the data preparation, handling, and lineage control, as well as model deployment, in particular in a distributed environment. Your personal skills include: Strong team spirit but capable More ❯

features like cross-provider LLM APIs or integration with the latest Machine Learning models Collaborate with research teams on innovative machine learning experiments Tech stack: Python, scikit-learn, TensorFlow, PyTorch, Java, Angular, AngularJS MLOps : Develop back-end capabilities for automating and monitoring ML model lifecycles while supporting cross-functional collaboration. Build tools to automate retraining, monitoring, and deployment of ML More ❯

large-scale data processing with Spark or Dask. Strong in Python, Scala, C# or Java, cloud SDKs and APIs. AI/ML expertise for pipeline efficiency, familiar with TensorFlow, PyTorch, AutoML, Python/R, and MLOps (MLflow, Kubeflow). Solid in DevOps, CI/CD automation with Bitbucket Pipelines, Azure DevOps, GitHub. Automate deployment of data pipelines and applications using More ❯

one or more Data Science or AI domains Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Cloud platforms (AWS/Azure/GCP) Experience with AI governance and responsible AI practices Understanding of More ❯

Demonstrated programming expertise in Python and experience working with different types of complex and/or multi-modal datasets. Solid understanding of at least one major deep learning framework (PyTorch, TensorFlow). Knowledge of machine learning principles and state-of-the-art modelling approaches. Track record of research (e.g. via publications) in the field of Responsible AI. Keenness to learn More ❯

research experience to the position such as post doctoral roles, a proven track record of publications, or contributions to machine learning codebases. Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas Strong knowledge of linear algebra, calculus and statistics Experience working in a scientific environment across disciplines (particularly biology, chemistry More ❯

Data Integration & ETL: Data Pipelining Tools: Apache NiFi, Apache Kafka, and Apache Flink. ETL Tools: AWS Glue, Azure Data Factory, Talend, and Apache Airflow. AI & Machine Learning: Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, and MXNet. AI Services: AWS SageMaker, Azure Machine Learning, Google AI Platform. DevOps & Infrastructure as Code: Containerization: Docker and Kubernetes. Infrastructure Automation: Terraform, Ansible, and AWS CloudFormation. More ❯

one or more Data Science or AI domains Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Cloud platforms (AWS/Azure/GCP) Experience with AI governance and responsible AI practices Understanding of More ❯

using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualisation. Strong knowledge of linear algebra, calculus and statistics. Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas. A passion for applying ML research to real world problems. Depending on your experience: project supervision, leadership, or management. More ❯