11 of 11 Permanent Keras Jobs in London

technical stakeholdersStrong programming experience in Python and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/tf/keras, langchain). R and C++ optional.Experience with version control (GitHub)ML experience with causality, Bayesian statistics & optimization, survival analysis, design of experiments, longitudinal analysis, surrogate models ...

Scenarios simulation Solid experience with Probabilistic Programming and Bayesian Methods Experience using machine learning frameworks such as scikit-learn, Pymc, TensorFlow, PyTorch, Databricks ML, Keras etc..., Experience with ML at scale Experience coordinating projects across diverse teams. Proven attention to detail, critical thinking, and the ability to work both independently ...

/model-based), model post-training and test-time adaptation. Preferred qualifications, capabilities, and skills Practical experience with ML libraries (e.g. PyTorch, TensorFlow/Keras, HuggingFace Transformers etc.), agent frameworks (e.g. LangGraph, Google ADK, etc.), common formal verification tools and languages (e.g., Coq/Isabelle/Lean, TLA+, Alloy ...

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 ...

solid understanding of MLOps, Git and CI/CD practices. Strong Python programming skills, including experience with Pandas, Scikit-learn and either PyTorch or Keras/TensorFlow. Proven experience developing time-series forecasting models, ideally within energy, commodities, financial markets or another data-intensive environment. Knowledge of SQL is essential ...

/model-based), model post-training and test-time adaptation. Preferred qualifications, capabilities, and skills Practical experience with ML libraries (e.g. PyTorch, TensorFlow/Keras, HuggingFace Transformers etc.), agent frameworks (e.g. LangGraph, Google ADK, etc.), common formal verification tools and languages (e.g., Coq/Isabelle/Lean, TLA+, Alloy ...

fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. 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 ...

techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc.Experience using specialized machine learning libraries e.g. 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 ...

production environments. Experience in customer-facing or consulting roles is strongly preferredStrong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deploymentEngineering best practices: You write ...

technical stakeholders Strong programming experience in Python and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/tf/keras, langchain). R and C++ optional. Experience with version control (GitHub) ML experience with causality, Bayesian statistics & optimization, survival analysis, design of experiments, longitudinal analysis ...

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 ...