1 to 25 of 395 Neural Network Jobs in England

Architecture & Hands-On Development Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure … record of shipping AI/ML products end-to-end (from prototype to production). Hands-on expertise building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems More ❯

technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values comprise of Stewardship, Best People, Client Value Creation, One Global Network, Respect for the Individual and Integrity. Year after year, Accenture is recognized worldwide not just for business performance but for inclusion and diversity too. "Across the globe, one thing … can drive your career in new and exciting ways. If you're looking for a challenging career working in a vibrant environment with access to training and a global network of experts, this could be the role for you. As part of our global team, you'll be working with cutting-edge technologies and will have the opportunity to … and also unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling 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 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 ❯

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 ❯

Ensure the system's scalability, flexibility, and efficiency to handle large algorithms. Algorithm Development: Develop and implement advanced AI algorithms and models, including machine learning, deep learning, and neural networks. Continuously evaluate and improve these algorithms to enhance system performance and accuracy. Data Integration: Identify relevant data sources and design methods for data collection, integration, cleansing, and transformation. … unsupervised, or reinforcement learning techniques. Implement evaluation methodologies to measure the performance and effectiveness of trained models. Fine-tune models based on feedback and data insights. Neural Network Design: Design and optimize deep learning neural networks for various AI tasks, such as natural language processing, computer vision, recommendation systems, and predictive analytics. Implement innovative architectures … science, Artificial Intelligence, or related field. Experience with large language models (LLMs) and prompt engineering. Experience in designing and developing AI systems, including machine learning, deep learning, and neural networks. Strong programming skills in languages such as Python, R, or Java. Familiarity with AI libraries, frameworks, and tools such as TensorFlow, PyTorch, or Keras. Proven understanding of cloud 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 ❯

including AI/Gen AI and machine learning. Experience analyzing large data sets, data cleaning, and statistical analysis. Proven experience with at least three machine learning algorithms (e.g., neural networks, logistic regression, random forests). Proficiency with Java and Python, understanding of data structures, algorithms, and software design patterns. Experience with AI/Gen AI frameworks like TensorFlow 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 ❯

with performing statistical analyses leading to the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of data More ❯

Social network you want to login/join with: We’re looking for a talented Research Data Scientist who expects more from their career. It’s a chance to extend and improve dunnhumby’s world-class science capabilities. It’s an opportunity to work with a market-leading business to explore new opportunities for us and influence global retailers. … from senior team members. Pick up new machine learning approaches, such as regularised regression, clustering or tree-based ensembles, graph-based approaches, natural language processing, and neural network techniques, and apply them on client data. Perform exploratory data analysis to characterise and visualise datasets. Extend and develop programming skills, in languages such as Python and Spark, to … the freedom to play, experiment, and learn. And we don’t just talk about diversity and inclusion. We live it every day – with thriving networks including dh Gender Equality Network, dh Proud, dh Family, dh One, and dh Thrive as the living proof. We want everyone to have the opportunity to shine and perform at your best throughout our 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 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 ❯

TensorFlow, and cloud ML tools (AWS SageMaker, Databricks). - Define best practices for model governance, monitoring, and retraining in production. - Lead R&D into emerging techniques (e.g., graph neural networks for inventory routing, GenAI for buyer personalisation). - Manage and mentor a Senior Data Scientist, fostering growth in model optimisation, MLOps, and stakeholder collaboration. - Coordinate with Data Engineering More ❯

TensorFlow, and cloud ML tools (AWS SageMaker, Databricks). - Define best practices for model governance, monitoring, and retraining in production. - Lead R&D into emerging techniques (e.g., graph neural networks for inventory routing, GenAI for buyer personalisation). - Manage and mentor a Senior Data Scientist, fostering growth in model optimisation, MLOps, and stakeholder collaboration. - Coordinate with Data Engineering 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 ❯

with performing statistical analyses leading to the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of data 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 ❯