51 to 75 of 217 PyTorch Jobs in London

industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks and concepts, with direct experience working with YOLO models being a major plus. More ❯

industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks and concepts, with direct experience working with YOLO models being a major plus. More ❯

rapidly evolving AI landscape, identifying and adopting new techniques, tools, and methodologies as appropriate. Requirements: Excellent programming ability in Python and good experience with machine learning libraries such as PyTorch (preferred), TensorFlow, OpenCV etc. Experience in deploying, maintaining, and optimising deep learning pipelines, focusing on efficiency, performance, and production maturity. Strong understanding of machine learning principles, deep learning techniques and More ❯

industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks and concepts, with direct experience working with YOLO models being a major plus. More ❯

computing, neural deep learning methods and/or machine learning PREFERRED QUALIFICATIONS Experience in using Python and hands on experience building models with deep learning frameworks like Tensorflow, Keras, PyTorch, MXNet - Prior experience in training and fine-tuning of Large Language Models (LLMs) - Knowledge of AWS platform and tools Our inclusive culture empowers Amazonians to deliver the best results for More ❯

NLP, LLM and graph analytics and hands-on experience and solid understanding of machine learning and deep learning methodsExtensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goalsExperience with big data More ❯

multi-faceted projects with competing deadlines. Expertise in Agile project management methodologies is a plus. Hands-on experience with a variety of machine learning frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch etc.) and AutoML technologies (e.g., Datarobot, Dataiku). Experience in deploying models at scale using cloud infrastructure is a plus. Proven ability to lead, mentor, and inspire teams of data More ❯

production-grade code and a good understanding of data engineering & MLOps Solid understanding of machine learning concepts, familiarity working with common frameworks such as sci-kit-learn, TensorFlow, or PyTorch Collaborative and humble great teammate with an ability to work wimulti-functionalnal teams, including technical and non-technical stakeholders Passion for learning new skills and staying up-to-date with More ❯

AI to enhance traditional natural language processing tasks, prompt engineering, integrating LLMs into machine learning workflows Strong Python skills and familiarity with libraries such as scikit-learn, pandas, NumPy, PyTorch or TensorFlow Advanced SQL skills and experience working with complex, high-volume datasets Practical experience with MLOps tools and practices for deploying and maintaining models Experience leading projects and collaborating More ❯

etc. Knowledge of machine learning modelling techniquesand how to fine-tunethosemodelseg.XGBoost, Deep Neural Networks, Transformers,ResNets,VAEs, GANs,Markov chains, etc. Experience using specialized machine learning librarieseg.Fastai,Keras,Tensorflow,pytorch, sci-kit learn,huggingface,etc. Must demonstrate the capacity of reading, understanding and implementingnew techniques in the field of machine learning as they emerge. Experience of using Cloud technologieseg.AWS, GCP More ❯

deep learning algorithms including the latest multimodal foundation models and generative AI frameworks' exploration, development, and implementation. Extensive experience with common machine learning Python frameworks such as TensorFlow and PyTorch; and Python libraries such as pandas, and computer vision libraries such as OpenCV. Experience in ONNX and TensorRT. Very comfortable working in Linux environment. Familiarity with software development tools and More ❯

deployment of ML models into production environments. Expertisein Python and relevant ML libraries Experience with various ML algorithms (e.g. supervised, unsupervised, reinforcement learning) and deep learning frameworks (e.g. TensorFlow,PyTorch). Experience developing generative AI applications and deploying them into production. Experience working with cloud platforms, ideally Google Cloud Platform (GCP) andBigQuery. Proficiencyin working with and querying global scale datasets. More ❯

for the last 10 years. You must be able to hold or gain a UK government security clearance. Preferred technical and professional experience Experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Familiarity with big data technologies (Hadoop, Spark). Background in data science, IT consulting, or a related field. AWS Certified Big Data or equivalent. IBM is committed More ❯

innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision Models. Use SQL to query and analyze the data. Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. Use machine learning and analytical techniques to create scalable solutions for business problems. Research and implement novel machine learning and statistical approaches. More ❯

CD pipelines (GitLab), and containerization (Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with More ❯

CD pipelines (GitLab), and containerization (Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with More ❯

CD pipelines (GitLab), and containerization (Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with More ❯

CD pipelines (GitLab), and containerization (Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with More ❯

Working with Data Scientists to deploy trained machine learning models into production environments Working with a range of models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch Experience with software engineering best practices and developing applications in Python. Technical experience of cloud architecture, security, deployment, and open-source tools ideally with one of the 3 major cloud More ❯

Computing, Computer Vision, or related technical discipline. Good knowledge of machine learning and computer vision algorithms. Familiarity with real-time imaging pipelines. Experience with scientific software packages such as PyTorch, OpenCV, Pandas, SciPy, NumPy, SciKit's, etc. Experience in standard software engineering practices including version control systems and software testing methodologies. Excellent oral and written communication skills. Analytical thinker, attentive More ❯

production-grade code and a good understanding of data engineering & MLOps Solid understanding of machine learning concepts, familiarity working with common frameworks such as sci-kit-learn, TensorFlow, or PyTorch Collaborative and humble team player with an ability to work with cross-functional teams, including technical and non-technical stakeholders Passion for learning new skills and staying up-to-date More ❯

how of designing and implemention synchronous, asynchronous and batch data processing operations Expert level programming skills in Python, along with experience in using relevant tools and frameworks such as PyTorch, FastAPI and Huggingface; strong programming skills in Java are a plus Expert level know-how of ML Ops systems, data pipeline design and implementation, and working with ML platforms (preferably More ❯

BASIC QUALIFICATIONS - 5+ years of hands-on experience optimizing AI infrastructure, with deep expertise in inference acceleration frameworks (e.g., vLLM, SGLang, TensorRT, etc.), model training and serving systems across PyTorch and TensorFlow ecosystems; - Advanced proficiency in Nvidia GPU performance optimization techniques, including memory management, kernel fusion, and quantization strategies for large-scale deep learning workloads; - Strong foundation in parallel computing More ❯

generative AI. Able to contextualise these techniques in industry settings, such as financial forecasting, operations optimisation, or customer segmentation. Comfortable with key ML frameworks (such as Scikit-learn, TensorFlow, PyTorch, Hugging Face) and data manipulation tools (Pandas, NumPy), as well as version control, containerisation, and ML deployment pipelines. Understands how to apply MLOps principles in production environments To be successful … experience in machine learning and/or data science roles Solid understanding of MLOps and production deployment practices Experience with Python and core ML libraries (e.g., Scikit-learn, Pandas, PyTorch, TensorFlow) Familiarity with cloud platforms and data infrastructure (e.g., AWS/GCP/Azure, SQL, ELT tools) Understanding of ethical frameworks, explainability, and governance in AI Engaging & Adaptable Communication: Ability More ❯

Are you passionate about solving complex problems with data? Join a fast-growing tech and software innovation company in the heart of London. We’re looking for a Data Scientist eager to apply advanced modelling, analytics, and machine learning to More ❯