17 of 17 Part-Time PyTorch Jobs in the UK

nStrong interpersonal skills and team working. \n \n\n DESIRABLE: \n \n \nDemonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). \n \n\n \n \nExperience with data storage ...

developing junior members of a team and wider community. \n \n\n DESIRABLE: \n \n \nDemonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g, GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). \n \n\n \n \nExperience with ...

developing junior members of a team and wider community. \n \n\n DESIRABLE: \n \n \nDemonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). \n \n\n \n \nExperience with ...

environments. \n Experience with MLOps practices and tools for managing the machine learning lifecycle. \n Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. \n Strong understanding of data preprocessing, feature engineering, and evaluation techniques. \n Experience working with large, complex datasets and applying statistical ...

Microsoft Azure) \n Knowledge of containerisation technologies like Docker and orchestration tools such as Kubernetes \n Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, or Java-based ML libraries) \n Strong problem-solving and communication skills \n\n Preferred Qualifications \n \n Experience with NLP, computer vision, or recommendation ...

engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \n\n Platform & Cloud Engineering: \n \n Architecture and delivery of scalable cloud ...

MLOps tooling - for versioning, reproducibility, observability etc. \n Experience monitoring, maintaining, enhancing existing models over an extended time period \n Extensive experience with pytorch and related data science python libraries (e.g. pandas) \n Experience of containerising applications or models for deployment (Docker) \n Experience with one of the major public ...

Science, Machine Learning, or related field. \n Experience building and deploying agentic AI applications in production environments. \n Expertise with ML frameworks such as PyTorch, TensorFlow, and scikit-learn. \n Proficiency in Python; experience writing comprehensive tests and building evaluation harnesses for agents and prompts. \n Hands-on experience with ...

based chatbots and agents \n Proven ability to build machine learning models and pipelines using Python and common ML and DL libraries (e.g. Pytorch, Tensorflow) from early conceptualisation to full deployment in scalable production environments \n Ability to design, deploy and maintain ML solutions on modern frameworks to meet functional ...

full cycle from design, experimentation, deployment, to monitoring and maintenance of machine learning models. \n Expertise in machine learning frameworks such as TensorFlow, PyTorch, PyTorch Lightning, or Scikit-learn. \n Proficiency in Python with a strong emphasis on code quality and reliability through comprehensive testing. \n Extensive experience with generative ...

real-world problems in industry or academia \n Strong Python programming skills and deep familiarity with the Python ecosystem (i.e. pandas, numpy, scikit-learn, pytorch, pip, pytest) \n Demonstrated ability to lead technical teams and initiatives with strong communication and stakeholder management skills \n\n Preferred qualifications, capabilities, and skills ...

practical implementations of ML algorithms such as clustering, decision trees, gradient descent etc. \n Hands-on experience with deep learning toolkits such as PyTorch, Transformers, HuggingFace. \n Strong knowledge of language models, prompt engineering, model finetuning, and domain adaptation. \n Familiarity with latest development in deep learning frameworks. \n Ability ...

deep learning methods and large language models. \n Extensive experience with ML/DL toolkits and libraries (e.g., Transformers, Hugging Face, TensorFlow, PyTorch, NumPy, scikit-learn, pandas). \n Demonstrated leadership in proposing and delivering AI/ML and GenAI solutions; ability to drive technical direction and influence stakeholders. … Depth in one or more: Natural Language Processing, Reinforcement Learning, Ranking/Recommendation, or Time Series Analysis. \n Additional familiarity with ML frameworks (e.g., PyTorch, Keras, MXNet, scikit-learn). \n Understanding of financial services or wealth management domains. \n Desirable: Contributions to open-source ML/LLM tooling; certifications ...

Solid understanding of fundamentals of statistics, optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.) \n Extensive experience with pytorch, numpy, pandas \n Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.) \n Able ...

Strong software development skills with proficiency in multiple programming languages (Python, JavaScript/TypeScript, etc.) \n Hands-on experience with machine learning frameworks (PyTorch, TensorFlow, or similar) \n Extensive daily use of generative AI tools for coding (Claude, Cursor, GitHub Copilot, or equivalent) \n Proven ability to deliver working prototypes ...

Expertise in transformer architectures (e.g., BERT, GPT, T5, Time‐Series Transformers). \n Strong hands-on experience with modern deep learning frameworks (PyTorch preferred). \n Solid grounding in machine learning fundamentals and statistical modelling. \n Familiarity with distributed training and GPU acceleration. \n A track record of delivering impactful ...

develop and deploy business-critical, data-intensive applications \n Extensive experience with AWS and Kubernetes \n Proficiency with lower-level libraries such as PyTorch and NumPy \n Hands-on experience implementing distributed, multi-threaded, and scalable applications \n Experience with automated building, testing, and deployment pipelines \n Familiarity with higher ...