201 to 225 of 2,944 TensorFlow Jobs in the UK

competitive coding background (e.g., ACM/ICPC, NOI/IOI, Top Coder, Kaggle). Technical Skills : Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch), Generative AI technologies, and libraries like Langchain, Weaviate, Langgraph, LlamaIndex. Track Record : Demonstrated success in applying data science and machine learning to solve More ❯

participation in GenAI or platform operations communities encouraged Strong understanding of AI governance, data privacy, and Responsible AI frameworks Experience with AI frameworks like TensorFlow, PyTorch, LangChain Leadership skills in cross-functional teams within complex ecosystems Familiarity with monitoring and telemetry tools (Prometheus, Grafana, Azure Monitor) Excellent communication and More ❯

. Strong expertise in specialized areas such as deep learning (DL) or natural language processing (NLP). Practical experience with ML platforms such as TensorFlow/Keras, PyTorch. Comfort with rapid prototyping and disciplined software development processes. Practical software engineering experience in collaborative project settings. Hands-on experience developing More ❯

experience. Strong expertise in deep learning, neural networks, and generative models (GANs, diffusion models). Practical experience with modern machine learning frameworks (e.g., PyTorch, TensorFlow). Advanced programming skills in Python . Strong problem-solving, analytical, and communication skills. Demonstrated ability to work effectively in multidisciplinary, fast-paced, research More ❯

solutions for real world problems. Mentor and coach less experienced colleagues. Skills, Knowledge and Expertise •Strong proficiency in Python, including libraries like Pandas, NumPy, TensorFlow, and PyTorch. Experience with Generative AI, Large Language Models (LLMs) and their practical applications. Expertise in data insights, analytics, and predictive modelling. Knowledge of More ❯

Proven expertise in AI/ML fields such as LLMs, Computer Vision, Generative AI, NLP, or foundational models. Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and familiarity with cloud-based computing platforms. Strong analytical, mathematical, and coding skills (e.g., Python, C++, or Java). First author in research More ❯

frameworks like Streamlit) for dashboard creation. Experience with Machine Learning model development and data science workflows (including frameworks such as scikit-learn, PyTorch, or TensorFlow). Experience in Quantitative Finance or strong interest in mathematical/financial modeling, derivatives pricing, or algorithmic trading. Familiarity with ETRM platforms (e.g., OpenLink More ❯

ICML, EMNLP, CVPR, etc.) and/or journals. Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc. Strong written and verbal communication skills to operate in a cross functional team environment. About Us: At Scale, we believe that the transition More ❯

in this role: • Proven experience in enterprise architecture, projects specifically in AI/ML systems design • Deep understanding of AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and cloud platforms (AWS, Azure, GCP) • Strong experience with data architecture, pipelines and governance (e.g., data lakes, ETL, MLOps) • Knowledge of More ❯

practical experience). - 2-3 years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. - Strong Python and Java programming skills and More ❯

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 More ❯

solutions for real world problems. Mentor and coach less experienced colleagues. Skills, Knowledge and Expertise ·Strong proficiency in Python, including libraries like Pandas, NumPy, TensorFlow, and PyTorch. Experience with Generative AI, Large Language Models (LLMs) and their practical applications. Expertise in data insights, analytics, and predictive modelling. Knowledge of More ❯

through multiple release cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with More ❯

through multiple release cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with More ❯

through multiple release cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with More ❯

through multiple release cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with More ❯

through multiple release cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with More ❯

of experience in data science or building LLM applications, especially in the cybersecurity domain. Strong practical experience in Python and tools like Scikit-Learn, TensorFlow, PyTorch, Keras, Pandas, Polars, Spark, and DuckDB . Demonstrated experience applying LLMs to cybersecurity or other high-complexity, domain-specific problems. Understanding of network More ❯

effectively and collaboratively in a global organization, across time zones, and across organizations. Understanding of deep learning, understanding of Machine Learning frameworks such as TensorFlow or PyTorch. Understanding of Information Security, Secure coding practices. Experience in building cloud and container native applications. Knowledge of DevOps and Agile practices. Excellent More ❯

ICML, EMNLP, CVPR, etc.) and/or journals Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc. Strong written and verbal communication skills to operate in a cross functional team environment PLEASE NOTE: Our policy requires a 90-day waiting More ❯

simulation tools (e.g., MATLAB/Simulink, Modelica, Ansys, or equivalent platforms) for modeling BESS components. Strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Experience with Linux command-line. Hands-on experience with cloud-based environments such as AWS, Azure, or GCP. Knowledge of More ❯

as Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named entity recognition (NER), topic modeling, semantic similarity, and knowledge graph construction. Demonstrated More ❯

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 More ❯

as Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named entity recognition (NER), topic modeling, semantic similarity, and knowledge graph construction. Demonstrated More ❯

data science. Strong understanding of AI/ML algorithms and techniques, including LLMs, GenAI, and automated AI systems. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data science libraries (e.g., NumPy, pandas, scikit-learn). Proficiency in Python, R, or other relevant programming languages. Proficiency in working with More ❯