11 of 11 Remote/Hybrid TensorFlow Jobs in the City of London

commercial optimisation. Required Skills and Experience Technical Skills Proficiency in Python and associated data science libraries (NumPy, Pandas, SciPy, scikit‐learn, PyTorch/TensorFlow). Strong experience with time‐series modelling (ARIMA, Prophet, LSTMs or similar). Understanding of optimisation methods (linear, mixed‐integer, reinforcement learning desirable). Strong ...

models together to achieve reliable results. True ML Fundamentals: You aren't limited to APIs. You have a background in training and evaluating models (TensorFlow/PyTorch/Keras/Scikit-learn) and know when a simple classifier is better than a generative model. Data Fluency: You understand ...

like Time-Series Forecasting , Probabilistic Modelling , or Combinatorial Optimization . Technical Proficiency: Expert-level Python skills with deep experience in libraries such as PyTorch, TensorFlow, or Scikit-learn . Problem Solving: A track record of applying theoretical concepts to solve messy, real-world business problems. Communication: Ability to explain ...

technologies Required Skills & Experience MSc or PhD in Computer Science, Machine Learning, Mathematics, Statistics, or similar Strong Python programming skills Experience with PyTorch, TensorFlow, or optimisation libraries (e.g. Gurobi, OR-Tools) Strong technical communication and stakeholder engagement skills Ability to write clean, efficient, maintainable code Interest in explaining ...

libraries such as Neo4j, NetworkX, GraphX, or similar. Strong programming skills in Python & PySpark, with experience in relevant machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch). Expertise in graph algorithms including shortest path, centrality, community detection, and graph embeddings. Experience with big data technologies and cloud platforms ...

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 ...

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 ...

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 ...

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 ...

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 ...

Safety classifiers Behaviour-shaping or reward-modelling components. LLM and multimodal fine-tunes Adversarial robustness defences Use Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX, HuggingFace). Contribute to creation of synthetic datasets, adversarial evaluation corpora, and scenario-based test sets. Help transition research outputs into scalable controls … grasp of core Responsible AI concepts: Bias and fairness Explainability Privacy-preserving ML. Robustness and uncertainty Nice to have: Experience with ML frameworks: PyTorch, TensorFlow, Flax/JAX, HuggingFace. Exposure to multimodal models (CLIP, Whisper, LLaVA, video transformers). Familiarity with safety benchmarks, adversarial testing, red teaming, or uncertainty ...