1 to 25 of 133 Remote Neural Network Jobs in England

to extract knowledge or insights to drive the future of artificial intelligence. What you will be doing: Applying and/or developing statistical modelling techniques (such as deep neural networks, Bayesian models, Generative AI, Forecasting), optimization methods and other ML techniques Converting data into practical insights Analysing and investigating data quality for identified data and communicate it to More ❯

to extract knowledge or insights to drive the future of artificial intelligence. What you will be doing: Applying and/or developing statistical modelling techniques (such as deep neural networks, Bayesian models, Generative AI, Forecasting), optimization methods and other ML techniques Converting data into practical insights Analysing and investigating data quality for identified data and communicate it to More ❯

workflows. Proficiency in Python and strong knowledge of ML algorithms-ranging from classical techniques to deep learning methods. Proven experience in training, fine-tuning, and deploying neural network models using frameworks like PyTorch, with a focus on optimizing performance and scalability for realworld applications. Demonstrated ability to mentor and lead technical teams, influence technical strategy, and drive More ❯

Engineer - Hybrid in London, UK Client Introduction: The company is a multinational Sweden SAAS product-based firm. Company Strength: 100+ Job Description: Design and implement Graph Neural Network (GNN) architectures for enterprise-scale applications. Develop and optimize vector embedding generation pipelines using ScaNN or similar ANN techniques. Train, fine-tune, and deploy ML/DL models using … facing experience of discovery, assessment, execution, and operations. Demonstrated excellent communication, presentation, and problem solving skills. Experience in project governance and enterprise customer management. Proficiency in building Graph Neural Networks (GNNs) using frameworks like DGL, PyTorch Geometric, or similar. Experience with ScaNN or other approximate nearest neighbor (ANN) techniques for vector similarity search. Hands-on experience with Google More ❯

Transformers, with a solid understanding of LLM architectures. You should be well-versed in various machine learning techniques, including supervised, unsupervised, and semi-supervised learning, as well as neural networks, support vector machines (SVM), tree-based methods, and deep learning algorithms. Knowledge of natural language understanding and generative AI is essential. The role requires proficiency in MLOps practices More ❯

and systems. Proficiency in Python and strong knowledge of ML algorithms, ranging from classical techniques to deep learning methods. Experience in training, fine-tuning, and deploying neural network models using frameworks like PyTorch. Expertise in containerization (e.g., Docker, Kubernetes, AWS EKS) and building scalable systems in cloud environments. Deep understanding of software development fundamentals, including automated testing More ❯

and systems. Proficiency in Python and strong knowledge of ML algorithms, ranging from classical techniques to deep learning methods. Experience in training, fine-tuning, and deploying neural network models using frameworks like PyTorch. Expertise in containerization (e.g., Docker, Kubernetes, AWS EKS) and building scalable systems in cloud environments. Deep understanding of software development fundamentals, including automated testing More ❯

and systems. Proficiency in Python and strong knowledge of ML algorithms, ranging from classical techniques to deep learning methods. Experience in training, fine-tuning, and deploying neural network models using frameworks like PyTorch. Expertise in containerization (e.g., Docker, Kubernetes, AWS EKS) and building scalable systems in cloud environments. Deep understanding of software development fundamentals, including automated testing More ❯

and differential privacy methods Familiarity with next-generation computing paradigms such as neuromorphic computing, brain-inspired architectures, or quantum machine learning Specialized knowledge in computational linguistics or graph neural networks for complex relational data Contributions to open-source machine learning projects or frameworks Please note: This role focuses on fundamental research that pushes beyond current AI paradigms to More ❯

and probabilistic representations for diverse ML tasks. Build and optimize tools for scalable probabilistic inference under memory, latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering, and business teams to build end-to-end modeling solutions. Conduct More ❯

and systems. Proficiency in Python and strong knowledge of ML algorithms, ranging from classical techniques to deep learning methods. Experience in training, fine-tuning, and deploying neural network models using frameworks like PyTorch. Expertise in containerization (e.g., Docker, Kubernetes, AWS EKS) and building scalable systems in cloud environments. Deep understanding of software development fundamentals, including automated testing More ❯

diffusion models Proficiency in programming languages such as Python, R, or Julia, and experience with ML frameworks like TensorFlow, PyTorch, or Keras Strong understanding of deep learning architectures, neural networks, and statistical modeling techniques Experience with cloud platforms (AWS, Azure, Google Cloud) and MLops tools for model deployment and management Excellent problem-solving skills with a strong ability More ❯

systems Exposure/experience in containerization technologies like Docker, Kubernetes, AWS EKS etc. Proficiency in ML algorithms, such as multi-class classification, decision trees, support vector machines, and neural networks (deep learning experience strongly preferred) Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g., AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage, etc. More ❯

or a related field, with 0-2 years working experience. Programming Language: Proficiency in Python. Solid understanding of data structures and algorithms. Proficient in Machine Learning, including understanding Neural Networks and foundational concepts of NLP tasks such as Tokenization, Named Entity Recognition (NER), Sentiment Analysis, Part-of-Speech (POS) Analysis. Experience or coursework in Deep Learning frameworks like More ❯

systems Exposure/experience in containerization technologies like docker, Kubernetes, AWS EKS etc. Proficiency in ML algorithms, such as multi-class classification, decision trees, support vector machines, and neural networks (deep learning experience strongly preferred) Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g., AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage, etc. More ❯

agentic workflows strongly preferred) Experience building/enhancing search and information retrieval systems Proficiency in ML algorithms, such as multi-class classification, decision trees, support vector machines, and neural networks (deep learning experience strongly preferred) Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g., AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage, etc. More ❯

such as engineering, mathematics, physics, or statistics. Experience in data science within financial services, insurance, or E-commerce (not essential). Familiarity with cloud-based deployment. Knowledge of neural networks, TensorFlow, CatBoost, XGBoost, SKlearn, or Pandas. Experience with API development. Proficiency in SQL. Background in software engineering or DevOps/MLOps. Understanding of CI/CD pipelines. Why More ❯

leveraging Large Language Models, as well as a deep understanding of how to implement solutions using Retrieval Augmented Generation (RAG) architecture Experience with graph machine learning (i.e. graph neural networks, graph data science) and practical applications thereof This is complimented by your experience working with Knowledge Graphs, ideally biological, and a familiarity with biological ontologies Experience with complex More ❯

Accountability Customer Focus Who you are You will need to be well-versed in various machine learning techniques, including supervised, unsupervised, and semi-supervised learning, as well as neural networks, support vector machines (SVM), tree-based methods, and deep learning algorithms. Knowledge in natural language understanding and generative AI will be essential. The role requires proficiency in MLOps More ❯

engineering, mathematics, physics, statistics Experience of data science in finance, insurance or Ecommerce is an advantage but not required Experience of deployment in a cloud environment Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas Experience with API development SQL experience Software engineering experience DevOps/MLOps experience Good working understanding of CI/CD Diversity More ❯

ideally working with data engineering. Proficiency in statistical analysis, machine learning, and data mining techniques. A good understanding of key machine learning models, including Gradient Boosting Machines (GBMs), Neural Networks and Large language models (LLMs). Hands-on experience with popular machine learning libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Knowledge of AWS products and More ❯

equivalent qualification, or other evidence of equivalent knowledge. Professional or research experience in some of the following: ML/NLP tools like PyTorch, Scikit-learn, Transformer architecture, and Neural Networks. Coding and software engineering in mainstream programming languages, such as Python. Statistical packages for data manipulation and in the development of predictive and prescriptive models (e.g., R, SAS More ❯

Social network you want to login/join with: Location: Bristol | Hybrid – 3 days WFH a week Role Type: Permanent Salary: As above, depending on experience About the Client: Based in Bristol, the client is a dynamic data science and technology company. Acquired by the biggest & most influential player in their sphere in 2020, they retain their independent identity … programming and data handling skills, particularly in Python or similar languages. Proven experience in developing data science solutions from proof of concept to implementation. Expertise in deep learning, neural networks, and working with unstructured data (e.g., text, images, video). Excellent communication and stakeholder management skills. Experience leading teams in a data/data science setting. Experience with More ❯

engineering, mathematics, physics, statistics Experience of data science in finance, insurance or Ecommerce is an advantage but not required Experience of deployment in a cloud environment Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas Experience with API development SQL experience Software engineering experience DevOps/MLOps experience Good working understanding of CI/CD If More ❯

Social network you want to login/join with: Audio AI & Digital Signal Processing Engineer, exeter col-narrow-left Client: Blue Signal Search Location: exeter, United Kingdom Job Category: Other - EU work permit required: Yes col-narrow-right Job Views: 5 Posted: 06.06.2025 Expiry Date: 21.07.2025 col-wide Job Description: Title: Audio AI & Digital Signal Processing Engineer Company Overview … problem-solving skills with the ability to implement robust, efficient, and scalable solutions. Excellent communication and collaboration skills in cross-functional environments. Preferred Experience: Expertise in neural network architectures and generative adversarial networks (GANs) for audio applications. Knowledge of secure audio processing techniques, watermarking, and cryptographic applications in AI. Experience with cloud-based AI/ML infrastructure More ❯