6 of 6 PyTorch Jobs in West London

experience in managing, coaching, and developing junior members of a team and wider community. D ESIRABLE : Demonstrable 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). Experience with data storage ...

Looking For in a Machine Learning Engineer Strong experience developing and deploying Machine Learning models using Python Experience with frameworks such as PyTorch, TensorFlow, Scikit-learn or XGBoost Knowledge of AWS cloud services including SageMaker, Lambda and S3 Experience with MLOps tools such as MLflow, Weights & Biases or Data Version ...

developing solutions with Large Language Models. Hands-on experience building applied ML models using Python and relevant libraries and frameworks (e.g., scikit-learn, XGBoost , PyTorch ). Desirable skills and experience: Experience designing evaluation frameworks for generative systems (e.g., LLM-as-a-judge, task-specific metrics). Experience with vector databases ...

shape what responsible AI security looks like in practice About you (skills/experience): You will bring A genuine understanding of deep learning (JAX, PyTorch, or TensorFlow), and you've worked with large-scale cloud training or inference infrastructure Think like an attacker on AI systems: prompt injection, model inversion ...

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

multimodal sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...