1 to 25 of 429 Permanent PyTorch Jobs in the UK

Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency, high-availability inference. Strategic … to production). Hands-on expertise building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on cloud platforms (AWS More ❯

algorithms and their practical applications, 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 data ingestion and processing. Proficiency More ❯

engineers as the team grows. Key Responsibilities Implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes). 2. Data & Infrastructure Build and maintain scalable … systems end-to-end. Strong hands-on expertise in building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks). Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face). Solid software engineering background: data structures, algorithms, distributed systems, and version control (Git). Knowledge of data-engineering concepts: SQL/noSQL, data pipelines More ❯

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: 💰 Competitive Salary More ❯

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: 💰 Competitive Salary More ❯

data preprocessing, and feature engineering. Proven experience building and deploying RAG systems and/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers , or TensorFlow. Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex). Hands-on experience with fine-tuning and distillation of More ❯

data preprocessing, and feature engineering. Proven experience building and deploying RAG systems and/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers , or TensorFlow. Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex). Hands-on experience with fine-tuning and distillation of More ❯

augmentation for textual analysis, with an interest in learning more. Experience working with commonly used data science libraries and frameworks, e.g. Spacy, pandas, numpy, scikit-learn, Keras/TensorFlow, PyTorch, LangChain, Huggingface transformers etc. Familiar with both on-premises and cloud-based platforms (e.g. AWS). Working understanding of ML Ops workflows and ability to perform basic model deployment without More ❯

lifecycle of ML projects, including initial conceptualization, data handling, model development, and deployment. Proficiency in programming languages, including Python. Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, etc. Experience developing Python APIs using tools such as FastAPI. Knowledge of database technologies (SQL, MongoDB, Databricks) and data pipeline tools. Familiar with ML CI/CD pipelines More ❯

in Python development, including use of scientific and data libraries such as NumPy, pandas, SciPy, or PySpark. Experience working with machine learning frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, or similar. Strong grasp of object-oriented software engineering principles, with a focus on maintainability and scalability. Experience with version control tools, particularly Git, in collaborative development environments. Understanding More ❯

in Python development, including use of scientific and data libraries such as NumPy, pandas, SciPy, or PySpark. Experience working with machine learning frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, or similar. Strong grasp of object-oriented software engineering principles, with a focus on maintainability and scalability. Experience with version control tools, particularly Git, in collaborative development environments. Understanding More ❯

related quantitative field. - 4+ years of experience in developing and deploying machine learning models, with a strong focus on generative AI techniques. - Proficiency in programming languages such as Python, PyTorch, or TensorFlow, and experience with deep learning frameworks. - Strong background in natural language processing, computer vision, or multimodal learning. - Ability to communicate technical concepts to both technical and non-technical More ❯

products. Key Responsibilities: Model Development, Implementation and Deployment: Develop and implement state-of-the-art models for computer vision problems including object detection, key-point estimation, segmentation; using Python, PyTorch, Ignite, OpenCV, AWS Research, prototype, and implement state-of-the-art machine learning algorithms Design and implement custom loss functions, evaluation metrics, and training procedures Contribute to model selection, architecture … to problems Technical skills: Excellence with Python Excellence with version control (Git) Proficiency with containerisation (Docker) Proficiency with data processing tools (Pandas, NumPy) Experience using machine learning frameworks like PyTorch or TensorFlow Familiarity with cloud platforms and their ML services Benefits: MOST IMPORTANT: Your career Mentorship from senior machine learning engineers and data scientists Access to cutting-edge tools, technologies More ❯

in machine learning. Optimization : Continuously improve machine learning infrastructure and production workflows. Strong technical foundation in machine learning and software engineering Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) Experience with cloud platforms (AWS, GCP, Azure) Experience with CI/CD pipelines for machine learning (e.g., Vertex AI) Familiarity with data processing tools like Apache Beam/ More ❯

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 processing frameworks. More ❯

qualifications to be successful in this role: • Proven experience in enterprise architecture, projects specifically in AI/ML systems design • Deep understanding of AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and cloud platforms (AWS, Azure, GCP) • Strong experience with data architecture, pipelines and governance (e.g., data lakes, ETL, MLOps) • Knowledge of regulatory and ethical considerations in AI systems More ❯

Natural Language Processing (NLP) and Large Language Models (LLMs) Hands-on experience with machine learning and deep learning methods. Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow. Experience in advanced applied ML areas such as GPU optimization, fine tuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search). Ability to work on tasks and More ❯

language processing (NLP) techniques 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, fine-tuning, and workflow orchestration. Skilled in More ❯

learning models for tasks like recommendations, segmentation, forecasting, and optimising marketing spend. Proficiency in Python, SQL, Bash, and Git, with hands-on experience in tools like Jupyter notebooks, Pandas, PyTorch, and more. Experience with A/B testing and other experimentation methods to validate model performance and business impact. Experience with cloud platforms (AWS, Databricks, Snowflake), containerisation tools (Docker, Kubernetes More ❯

and architectures such as ReAct for building more effective autonomous agents for complex property management workflows and decision-making processes Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch), LLM orchestration tools (LangChain, LangGraph), MLOps practices and tooling (such as MLflow, Kubeflow, or similar), vector databases, and cloud platforms (AWS, Azure, GCP) with their AI/ML offerings Preferably More ❯

and architectures such as ReAct for building more effective autonomous agents for complex property management workflows and decision-making processes Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch), LLM orchestration tools (LangChain, LangGraph), MLOps practices and tooling (such as MLflow, Kubeflow, or similar), vector databases, and cloud platforms (AWS, Azure, GCP) with their AI/ML offerings Preferably More ❯

Associate Director, Data Science and Innovation Are you a data science leader passionate about leveraging AI and machine learning to drive innovation in financial markets and transaction banking? Standard Chartered Bank is on an ambitious journey to embed cutting-edge More ❯

Junior AI Engineer Our client is a rapidly growing tech-driven company based in central Manchester, leveraging artificial intelligence to build smarter, more efficient solutions across their digital platforms. With recent investment and a growing data team, they're looking More ❯

products. Key Responsibilities: Model Development, Implementation and Deployment: Develop and implement state-of-the-art models for computer vision problems including object detection, key-point estimation, segmentation; using Python, PyTorch, Ignite, OpenCV, AWS Research, prototype, and implement state-of-the-art machine learning algorithms Design and implement custom loss functions, evaluation metrics, and training procedures Contribute to model selection, architecture … to problems Technical skills: Excellence with Python Excellence with version control (Git) Proficiency with containerisation (Docker) Proficiency with data processing tools (Pandas, NumPy) Experience using machine learning frameworks like PyTorch or TensorFlow Familiarity with cloud platforms and their ML services Benefits: MOST IMPORTANT: Your career Mentorship from senior machine learning engineers and data scientists Access to cutting-edge tools, technologies More ❯

testing, monitoring, and continuous integration Requirements: 3+ years of experience in software engineering, with a focus on AI/ML integration Proficiency in Python and ML frameworks such as PyTorch or TensorFlow Strong grasp of software architecture and microservices Experience working in containerised environments (Docker, Kubernetes) Familiarity with CI/CD pipelines, secure deployment practices and cloud deployment (e.g., AWS More ❯