101 to 125 of 223 PyTorch Jobs in London

Data Science ? Experience as a trainer, instructor, or consultant in a technical field ? Strong communication and presentation skills for diverse learners ? Familiarity with AI tools such as Python, TensorFlow, PyTorch, Generative AI models (ChatGPT, LLMs) is desirable What's On Offer? ? Competitive daily rate: £400 - £500 p/d ? Flexible schedule with remote delivery options ? Opportunity to work with a More ❯

a proven commercial experience delivering AI/ML projects end-to-end in production environments Strong Python skills with hands-on use of ML libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI More ❯

cross-functional teams to turn real-world problems into working software. 🛠️ What You Bring: Fluency in Python and strong grasp of ML/DL concepts. Experience with frameworks like PyTorch, TensorFlow, Scikit-learn, and HuggingFace Transformers. Comfort designing production-ready AI systems—optimising for speed, cost, and value. Passion for leveraging AI assistants and agentic tools to maximise impact. Bonus More ❯

cross-functional teams to turn real-world problems into working software. 🛠️ What You Bring: Fluency in Python and strong grasp of ML/DL concepts. Experience with frameworks like PyTorch, TensorFlow, Scikit-learn, and HuggingFace Transformers. Comfort designing production-ready AI systems—optimising for speed, cost, and value. Passion for leveraging AI assistants and agentic tools to maximise impact. Bonus More ❯

statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression, classification, clustering, time-series forecasting). Practical experience with Keras or PyTorch is required. Full-Stack Deployment: Demonstrable experience taking models to production, including building and deploying APIs with FastAPI and using Vertex AI for ML workflows. Visualization & Communication: Ability to create More ❯

with a strong interest and practical experience in applied AI or machine learning. Strong programming skills in Python (or similar languages) with familiarity in AI/ML libraries (TensorFlow, PyTorch, etc.). Experience working with large-scale models (LLM/VLM) and deploying AI-driven solutions into production. Solid understanding of cloud technologies, containerization, and building scalable AI applications. Ability More ❯

NLP, Information Retrieval, or Conversational AI Experience with large language models and retrieval-augmented generation Research background in conversational systems or dialogue management Experience with modern deep learning frameworks (PyTorch, TensorFlow) Excellent written and verbal communication skills PREFERRED QUALIFICATIONS Knowledge of multi-agent systems and tool-use frameworks Experience with e-commerce or shopping domain applications Publications at top venues 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 ❯

Master's, PhD or equivalent experience in a technical field (e.g., Maths, Statistics, Computer Science, Physics, Chemistry, Biomedical Engineering). Python programming in a modern deep learning framework, e.g. PyTorch or JAX. Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling. Capable of designing, executing and reporting from ML experiments. Mathematics skills to support the above: calculus, probability theory More ❯

art modifications for transformer architectures as well as alternative approaches. Your expertise should emphasize techniques that enhance model intelligence, efficiency, and scalability within fine-tuning workflows. Strong expertise in PyTorch and Hugging Face libraries with practical experience in developing fine-tuning pipelines, continuously adapting models to new data, and deploying these refined models in production on target platforms. Demonstrated ability More ❯

Experience designing systems in modern cloud environments (e.g. AWS, GCP) Technologies and Tools Python ML and MLOps tooling (e.g. SageMaker, Databricks, TFServing, MLflow) Common ML libraries (e.g. scikit-learn, PyTorch, TensorFlow) Spark and Databricks AWS services (e.g. IAM, S3, Redis, ECS) Shell scripting and related developer tooling CI/CD tools and best practices Streaming and batch data systems (e.g. More ❯

on experience in both machine learning engineering (training ML models), and who have full-stack SWE experience - both backend and frontend engineering. Ideal candidates should have experience with Python, Pytorch, JS and React and similar frameworks. Responsibilities: Carry out software development and research for designing automated LLM agents for coding, ML engineering and research tasks, including building agent analysis tools More ❯

scale The Tech Stack AI/ML : Multi-agent frameworks, fine-tuning, reinforcement learning, advanced RAG Platform : AWS serverless, event-driven architecture, microservices Languages : Python, with modern ML libraries (PyTorch, TensorFlow) What They're Looking For 5+ years developing AI/ML systems beyond basic integration Deep understanding of agent architectures and distributed AI systems Computer science fundamentals - algorithms, system More ❯

GANs, Diffusion Models). Strong understanding of financial use cases: fraud detection, algorithmic trading, regulatory tech, and personalisation. Experience with cloud-native AI platforms (Azure, AWS, GCP) and frameworks (PyTorch, TensorFlow). Exceptional stakeholder management and communication skills. Track record of building and scaling high-performing AI teams. This role is an urgent requirement, there are limited interview slots left More ❯

machine learning systems Qualifications Strong understanding of machine learning principles, especially in the context of LLMs. Solid proficiency in Python, including machine learning packages like Jax/Tensorflow or PyTorch Experience with large-scale data infrastructure Strong verbal and written communication skills with the ability to work effectively across internal and external organisations and virtual teams. Would be a plus More ❯

to model deployment , Experience with some of the following tools and technologies (or an eagerness to learn): , Python and its data science ecosystem (e.g., pandas, scikit-learn, TensorFlow/PyTorch) , Statistical methods and machine learning (e.g., A/B testing, model validation) , Data pipelining tools like SQL, dbt, BigQuery, or Spark , A strong communicator with the ability to communicate technical More ❯

ended problems. (Desirable) Experience designing and conducting experiments in a social science or human-computer interaction (HCI) context. Strong proficiency in Python and common ML libraries/frameworks (e.g., PyTorch, JAX, TensorFlow, Hugging Face). (Desirable) A track record of publications in relevant AI or interdisciplinary conferences. A strong scientific mindset geared towards rigorous experimentation, coupled with the engineering discipline More ❯

practices Experience in CI/CD (e.g. CircleCI). Preferred Qualifications Familiarity and enthusiasm about AI-based coding solutions (e.g. Cursor, Windsurf, etc.) Familiarity with ML concepts and frameworks (PyTorch). Experience in large-scale processing of multimodal computer vision data (images, videos, captioning, etc) for ML purposes. Experience in Structure-from-Motion for large-scale 3D reconstruction of image More ❯

time ML applications Maintain and improve recommender systems Build and work with modern API and microservices architectures Qualifications Strong foundation in Python Experience with machine learning, familiar with Huggingface, Pytorch, and similar ML tools and packages Familiarity with deploying and scaling ML models in the cloud, particularly with AWS and SageMaker Understanding of DevOps processes and tools: CI/CD More ❯

state-of-the-art models for a variety of computer vision tasks, including detection, segmentation, and classification. Writing highly efficient, scalable, and production-ready code in Python using the PyTorch framework. Optimizing models for high-performance inference on diverse hardware using tools like NVIDIA TensorRT, OpenVINO, and ONNX. Managing and processing large-scale datasets and implementing advanced data augmentation strategies. More ❯

state-of-the-art models for a variety of computer vision tasks, including detection, segmentation, and classification. Writing highly efficient, scalable, and production-ready code in Python using the PyTorch framework. Optimizing models for high-performance inference on diverse hardware using tools like NVIDIA TensorRT, OpenVINO, and ONNX. Managing and processing large-scale datasets and implementing advanced data augmentation strategies. More ❯

state-of-the-art models for a variety of computer vision tasks, including detection, segmentation, and classification. Writing highly efficient, scalable, and production-ready code in Python using the PyTorch framework. Optimizing models for high-performance inference on diverse hardware using tools like NVIDIA TensorRT, OpenVINO, and ONNX. Managing and processing large-scale datasets and implementing advanced data augmentation strategies. More ❯

state-of-the-art models for a variety of computer vision tasks, including detection, segmentation, and classification. Writing highly efficient, scalable, and production-ready code in Python using the PyTorch framework. Optimizing models for high-performance inference on diverse hardware using tools like NVIDIA TensorRT, OpenVINO, and ONNX. Managing and processing large-scale datasets and implementing advanced data augmentation strategies. More ❯

Artificial Intelligence, Machine Learning, or a related scientific field, or equivalent industry experience. Strong programming skills, especially in Python, with hands-on experience using deep learning frameworks such as PyTorch or similar libraries. Proven experience applying classical and deep learning algorithms to biological problems, ideally in domains such as RNA design, optimization, and molecular modeling. Strong foundation in biology, biochemistry More ❯

to model deployment. Experience with some of the following tools and technologies (or an eagerness to learn): Python and its data science ecosystem (e.g., pandas, scikit-learn, TensorFlow/PyTorch). Statistical methods and machine learning (e.g., A/B testing, model validation). Data pipelining tools like SQL, dbt, BigQuery, or Spark. A strong communicator with the ability to More ❯