26 to 50 of 333 PyTorch Jobs in England

or a related field. 5+ years' experience in data science, with proven success delivering end-to-end projects. Proficiency in Python, R, SQL, and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong skills in data storytelling, problem-solving, and working with both structured and unstructured data. Experience with APIs, cloud platforms (especially AWS), and MLOps practices is a More ❯

of-the-art NLP and ML techniques Mastery of several ML frameworks and libraries, with the ability to architect complex ML systems from scratch Extensive experience with Python and PyTorch Track record of contributing to the successful delivery of robust, scalable and production-ready ML models, with a focus on optimising performance and efficiency Experience with the full ML development More ❯

and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, ResNets, VAEs, GANs, Markov chains, etc. Experience using specialized machine learning libraries eg. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. Experience of using Cloud technologies More ❯

and our enterprise API About You Masters or PhD in Computer Science (or equivalent proven track record) 4 + years working on large-scale ML codebases Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed/FSDP/SLURM/K8s) Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops Strong software More ❯

. Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. Understanding of personalization More ❯

. Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. Understanding of personalization More ❯

tooling: Git, Unix/Linux, Docker. Plus, a strong opinion on your IDE/editor of choice is welcome Familiarity with modern machine learning tools, for instance TensorFlow, Keras, PyTorch or SKLearn. Commercial experience with these is not essential. Excellent communication skills; both in customer-facing and internal team communication. Knowledge of MLOps is not essential, but some awareness of More ❯

practical application. Proven track record of leading the development and deployment of complex AI models into production environments. Familiarity with MLOps principles, modern AI/ML frameworks (e.g., TensorFlow, PyTorch), and cloud-based AI platforms. Investment Banking Acumen: Strong understanding of Investment Banking products, deal lifecycles, advisory processes, and regulatory landscapes. Ability to identify specific pain points and opportunities where More ❯

NumPy, scikit-learn, etc.). Comfort working with large and complex datasets, including data wrangling and exploratory analysis. Experience (or strong interest) in GPU-powered model development (e.g. using PyTorch, TensorFlow, JAX, or similar). Demonstrated ability to work independently on real-world projects. Strong communicator, with the ability to share ideas, take feedback, and collaborate effectively. A master’s More ❯

NumPy, scikit-learn, etc.). Comfort working with large and complex datasets, including data wrangling and exploratory analysis. Experience (or strong interest) in GPU-powered model development (e.g. using PyTorch, TensorFlow, JAX, or similar). Demonstrated ability to work independently on real-world projects. Strong communicator, with the ability to share ideas, take feedback, and collaborate effectively. A master’s More ❯

across departments and navigate large organisations. Strong project management and organizational skills. Proficiency in programming languages such as Python, Java and Scala, and experience with ML frameworks like TensorFlow, PyTorch, and scikit-learn. Experience with cloud platforms (e.g., AWS), big data technologies (e.g., Spark) as well as other technologies used to deploy models to production (e.g., Kubernetes, GHA, Airflow, Docker More ❯

and GenAI technologies for specific scenarios, estimate the time, cost and outcomes of different solutions, and apply effective risk management to ensure prompt delivery • Deep experience in Python and PyTorch/TensorFlow. • Industry experience in building scalable, production-grade AI services and automated data pipelines with tools like Docker, Kubernetes, and cloud platforms (e.g., AWS). • Proven ability to collaborate More ❯

scientists and engineers to turn research ideas into working products Required Qualifications BSc, MSc or PhD in Computer Science, Data Science or a related field Strong Python skills with PyTorch, HuggingFace Transformers and Datasets Proven track record fine-tuning and serving large language models in real-world settings Hands-on experience building pipelines with reinforcement-learning algorithms such as PPO More ❯

machine learning engineers, AI researchers, and data scientists, driving end-to-end AI solution delivery. Designing and implementing AI architectures across cloud and on-prem environments, leveraging tools like PyTorch, TensorFlow, and Hugging Face. Spearheading the development of production ready AI models, from foundational LLMs to custom built cognitive agents, solving real world business challenges. Establishing scalable ML/AI More ❯

Data Science with reasonable industry experience, or an MS with significant industry or research experience in the field • Extensive 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 goals • Experience with big More ❯

Strong Statistical Analysis & Data Evaluation, youre comfortable developing or learning to develop custom metrics, identify biases, and quantify data quality. Strong Python skills for Data & Machine Learning, familiarity with PyTorch and TensorFlow. Experience with distributed computing and big data scaling ML pipelines for large datasets. Familiarity with cloud-based deployment (such AWS, GCP, Azure, or Modal). Experience in fast More ❯

data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning More ❯

responses and cancelable endpoints to support real-time and interactive use cases. Preferred skills and experience Machine learning experience : Familiarity with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn. Natural Language Processing (NLP) : Experience with NLP techniques and tools, such as spaCy or NLTK. Distributed systems : Knowledge of distributed systems and experience with tools like Kubernetes More ❯

programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning More ❯

programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning More ❯

learning or engineering roles. Strong expertise in Machine Learning and Deep Learning, with exposure to Reinforcement Learning as a plus. Proficiency in Python and modern ML libraries (e.g., TensorFlow, PyTorch, JAX, or Keras). Experience with version control systems (GitHub, GitLab) and knowledge of clean, maintainable coding practices. Familiarity with CI/CD pipelines for automating ML workflows. Ability to More ❯

natural language processing and computer vision) in industry. Master, PhD degree or equivalent experience in applied mathematics, computer science, bioinformatics or related scientific fields. Proficiency in software engineering (Python, Pytorch, JAX, Docker, Linux). A willingness to learn and develop skills in computational genomics including building an understanding of biological concepts related to cancer. Excellent communication skills in English. Appropriate More ❯

we are looking for: Essential: UK SC Clearance or the ability obtain it Demonstrable experience developing a variety of machine learning models Strong grasp of machine learning frameworks (e.g. PyTorch, Tensorflow) Knowledge of machine learning architectures, loss functions, tools and techniques Experience training machine learning models, including hyperparameter tuning and optimizing model performance Experience with (or at least exposure to More ❯

landscape and uses cases. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and More ❯

landscape and uses cases. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and More ❯