76 to 100 of 247 PyTorch Jobs in the UK

models (LLMs), building RAG pipelines, and experimenting with agentic AI workflows. Strong Python development skills and familiarity with modern ML and NLP frameworks and tooling (e.g. Hugging Face, spaCy, PyTorch, Scikit-learn). Familiarity with Kubernetes and infrastructure for deploying and scaling ML models is a plus. Exposure to systems integration challenges (e.g. connecting ML workflows with data stores like 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 ❯

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 ❯

to productise the AI models Ensure customers are successful in their use of our AI functionality Experience Key experience: At least five years of experience in frameworks such as PyTorch and Tensorflow Two years of experience in object detection models, including but not limited to YOLO, Faster R-CNN, and VIT Experience in training, fine-tuning, quantisation, and deploying computer More ❯

and industry advances in temporal modeling and deep learning. Document methods and support reproducibility, validation, and publication where appropriate. Essential Requirements Strong programming and modeling skills in Python (NumPy, PyTorch or TensorFlow, SciPy, pandas). In-depth knowledge of machine learning for time-series, including RNNs, LSTMs, GRUs, transformers, attention mechanisms. Solid understanding of probabilistic models (HMMs, Bayesian inference, graphical More ❯

early team member. What We’re Looking For 2+ years’ experience as a Machine Learning Engineer, Data Scientist, or similar role. Strong background in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with building and deploying ML models into production. Knowledge of APIs, data pipelines, and cloud infrastructure (AWS, GCP, or Azure). Strong problem-solving skills and More ❯

early team member. What We’re Looking For 2+ years’ experience as a Machine Learning Engineer, Data Scientist, or similar role. Strong background in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with building and deploying ML models into production. Knowledge of APIs, data pipelines, and cloud infrastructure (AWS, GCP, or Azure). Strong problem-solving skills and More ❯

early team member. What We’re Looking For 2+ years’ experience as a Machine Learning Engineer, Data Scientist, or similar role. Strong background in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with building and deploying ML models into production. Knowledge of APIs, data pipelines, and cloud infrastructure (AWS, GCP, or Azure). Strong problem-solving skills and More ❯

early team member. What We’re Looking For 2+ years’ experience as a Machine Learning Engineer, Data Scientist, or similar role. Strong background in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with building and deploying ML models into production. Knowledge of APIs, data pipelines, and cloud infrastructure (AWS, GCP, or Azure). Strong problem-solving skills and More ❯

early team member. What We’re Looking For 2+ years’ experience as a Machine Learning Engineer, Data Scientist, or similar role. Strong background in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with building and deploying ML models into production. Knowledge of APIs, data pipelines, and cloud infrastructure (AWS, GCP, or Azure). Strong problem-solving skills and More ❯

relevant discipline. Strong background in Machine Learning and/or statistical signal processing applied to sequential/sensor data. Proficiency in Python with experience in frameworks such as TensorFlow, PyTorch, or scikit-learn. Demonstrated expertise in developing ML solutions for real-world problems. Desirable Experience (one or more areas): RF communications, radar, sonar, or electronic warfare. Tracking, sensor data fusion More ❯

relevant discipline. Strong background in Machine Learning and/or statistical signal processing applied to sequential/sensor data. Proficiency in Python with experience in frameworks such as TensorFlow, PyTorch, or scikit-learn. Demonstrated expertise in developing ML solutions for real-world problems. Desirable Experience (one or more areas): RF communications, radar, sonar, or electronic warfare. Tracking, sensor data fusion More ❯

. Proficiency in containerization and orchestration (Docker, Kubernetes). Expertise in managing and optimizing cloud-based systems at scale. Preferred Skills Familiarity with Python (Jupyter) and ML frameworks (e.g., PyTorch). Knowledge of monitoring tools (Prometheus, Grafana). Experience with cloud-based databases (RDS, Aurora, Redshift, Cloud SQL, etc.) and visualization tools (QuickSight, Superset). Understanding of CI/CD More ❯

solutions. Strong engineering fundamentals, including DevOps, CI/CD, and secure ML/AI pipelines (DevSecOps). Highly proficient in Python, SQL, and key AI/ML frameworks (e.g., PyTorch, TensorFlow). Ability to mitigate model hallucination, optimise performance, and lead governance around AI risk and ethical deployment. Ready to Transform the Future? This a career-defining opportunity working within More ❯

solutions. Strong engineering fundamentals, including DevOps, CI/CD, and secure ML/AI pipelines (DevSecOps). Highly proficient in Python, SQL, and key AI/ML frameworks (e.g., PyTorch, TensorFlow). Ability to mitigate model hallucination, optimise performance, and lead governance around AI risk and ethical deployment. Ready to Transform the Future? This a career-defining opportunity working within More ❯

solutions. Strong engineering fundamentals, including DevOps, CI/CD, and secure ML/AI pipelines (DevSecOps). Highly proficient in Python, SQL, and key AI/ML frameworks (e.g., PyTorch, TensorFlow). Ability to mitigate model hallucination, optimise performance, and lead governance around AI risk and ethical deployment. Ready to Transform the Future? This a career-defining opportunity working within More ❯

building and deploying advanced ML and Generative AI models in production (including RAG Architecture) Deep technical proficiency with LLMs, NLP, Python, SQL, and major AI/ML frameworks (e.g., PyTorch, TensorFlow). Strong understanding of AI engineering fundamentals including DevOps, CI/CD, MLOps, and DevSecOps. Experience building AI governance frameworks to address ethical risk, model accuracy, and regulatory compliance. More ❯

building and deploying advanced ML and Generative AI models in production (including RAG Architecture) Deep technical proficiency with LLMs, NLP, Python, SQL, and major AI/ML frameworks (e.g., PyTorch, TensorFlow). Strong understanding of AI engineering fundamentals including DevOps, CI/CD, MLOps, and DevSecOps. Experience building AI governance frameworks to address ethical risk, model accuracy, and regulatory compliance. More ❯

You'll Bring: Degree in Computer Science, AI, ML, or a related field. Experience in developing and deploying AI/ML solutions. Proficiency in Python and ML frameworks (TensorFlow, PyTorch). Strong understanding of LLMs, NLP, and machine learning algorithms. MLOps knowledge and experience with version control systems. Back-end engineering skills in Python or Node.js (willingness to upskill in More ❯

technology: OCR, Object Detection and LLM analysis implementation * Machine Learning & AI Libraries including: o Transformers/Hugging Face for working with pre-trained LLMs, fine-tuning, and inference o PyTorch for deep learning model development and training o OpenCV for computer vision tasks and image preprocessing in object detection o PIL/Pillow for image manipulation and format conversion o More ❯

technology: OCR, Object Detection and LLM analysis implementation * Machine Learning & AI Libraries including: o Transformers/Hugging Face for working with pre-trained LLMs, fine-tuning, and inference o PyTorch for deep learning model development and training o OpenCV for computer vision tasks and image preprocessing in object detection o PIL/Pillow for image manipulation and format conversion o More ❯

core machine learning libraries such as Scikit-learn, XGBoost, or LightGBM to structured data problems. The role may also involve developing and deploying deep learning models using frameworks like PyTorch or TensorFlow where appropriate. Experience with version control (Git) and applying software engineering best practices is essential. Familiarity with MLOps principles and integrating cloud-based data platforms like Snowflake 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 ❯

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 ❯