1 to 25 of 58 Neural Network Jobs in England

and experimentation. You’ll work within multidisciplinary teams to develop, validate and deploy machine learning models to meet challenging customer requirements. Key Responsibilities Develop and train neural network models using frameworks such as PyTorch and TensorFlow Select and adapt model architectures to meet specific project requirements Build, curate, and manage training datasets, including data augmentation, feature extraction … technical and non-technical audiences Requirements Degree in Computer Science, Engineering, Mathematics, or related field Strong development skills in Python and C/C++ Experience with neural network architectures including RNNs, transformers, and vector quantisation In-depth knowledge of machine learning architectures and training algorithms Experience in model training, quantisation, and conversion for inference Hands-on experience More ❯

Requirements Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science , or a related technical field. Strong understanding of core ML concepts (supervised/unsupervised learning, neural networks, NLP, etc.). Hands-on experience with frameworks such as TensorFlow , PyTorch , or scikit-learn . Proficiency in Python and familiarity with common data science libraries (NumPy, pandas More ❯

for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

with engineering and data teams to ensure scalable solutions Requirements Extensive experience in data science, including applied statistics and machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms More ❯

on technical tasks. Familiarity with Agile methodologies and experience working in Agile teams. Key Technical Skills Python (Flask/FastAPI, OOP, unit testing). Machine learning model experience (Neural networks, Random forests etc.). Terraform or similar Infrastructure as Code (IaC) tools. GCP, AWS, or Azure. Docker and containerised deployments. CI/CD pipelines. Git based development. SQL. More ❯

Lead data sourcing, integration, and transformation pipelines to fuel high-quality model training. Model Lifecycle Management : Own the full lifecycle — training, evaluation, deployment, monitoring, and retraining. Neural Network Design : Push the boundaries of NLP, computer vision, and recommendation systems with custom deep learning architectures. Enterprise Integration : Embed Gen AI into core business platforms via APIs, microservices, and … with continuous experimentation and adoption of emerging Gen AI frameworks. What You Bring Must-Have Skills Proven experience designing and deploying Gen AI systems using LLMs, transformers, and neural networks Expert-level Python; strong in R, Java, or C++ Hands-on with TensorFlow, PyTorch, Keras, Hugging Face, LangChain Cloud-native mindset: AWS, Azure, GCP + Docker, Kubernetes, CI More ❯

where their work genuinely moves the needle. Machine Learning Engineer, LLM Engineer, AI Engineer, Artificial Intelligence, Deep Learning, NLP, Natural Language Processing, Large Language Models, Generative AI, GenAI, Neural Networks, PyTorch, TensorFlow, Hugging Face, OpenAI, LangChain, RAG, Retrieval-Augmented Generation, Python, Data Science, AI Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google More ❯

or streamlining and automating data refresh processes. What you'll be working on Building and improving price optimisation machine learning pipelines including data validation, classification, clustering, predictive modelling, neural networks, statistical model evaluation, optimisation, and process automation. Preparing presentations for clients based on data and market insights Interacting with clients on a regular basis and providing pricing guidance More ❯

data visualisation skills using tools like Tableau, Spotfire, Power BI etc. Expertise in predictive modelling, including both parametric (e.g. logit/probit) and non parametric (e.g. random forest, neural net) techniques as well as wider ML techniques like clustering/random forest (desirable). Tech Stack: SQL, Python, R, Tableau, AWS Athena + More! More information: Enjoy fantastic More ❯

and Computer Vision. Take ownership of developing, training and productionising machine learning lifecycles, adhering to best practices, security needs and quality assurance. Developing deep learning architectures and implementing neural networks tailored to complex use cases. Implementing and maintaining MLops workflows Collaborating closely with Data Engineers and DevOps teams to support continuous integration, deployment, and automation. Exploring new tools More ❯

self-taught AI/ML practitioner with relevant projects An early-career technologist seeking formal qualifications and impactful work Essential skills: Strong foundation in AI/ML (e.g. neural networks, NLP, transformers) Proficiency in Python and ML frameworks (e.g. PyTorch, TensorFlow) Familiarity with LLM APIs and open-source models (e.g. GPT, Claude, Whisper) Understanding of agentic AI frameworks More ❯

development experience in Python and machine learning techniques (production or laboratory) Strong knowledge of Python data science packages: Pandas, numpy, scikit-learn, pytorch Experience with Machine Learning techniques: Neural Networks, XGBoost Understanding of pipelines and deployment technologies: Azure DevOps, Docker, GitHub Actions Good understanding of architecture and design patterns Excellent verbal and written communication skills Strong troubleshooting and More ❯

e.g., NumPy, pandas, scikit-learn, PyTorch) and open-source contributions (especially Python-based) would be a bonus. Familiarity with CUDA, GPU-based computations, end-to-end neural network training, MLOps, and academic research in machine learning are also beneficial. Experience configuring and maintaining cloud infrastructure including network infrastructure, compute, access control policies, load balancers, Public Key More ❯

e.g., NumPy, pandas, scikit-learn, PyTorch) and open-source contributions (especially Python-based) would be a bonus. Familiarity with CUDA, GPU-based computations, end-to-end neural network training, MLOps, and academic research in machine learning are also beneficial. Experience configuring and maintaining cloud infrastructure including network infrastructure, compute, access control policies, load balancers, Public Key More ❯

grow Experience Implementing machine learning in production using in-house or third-party data science platforms Building machine learning models; experience in Natural Language Processing, Time-Series prediction, Neural Networks and Recommendation Systems will be advantageous. You possess excellent communication skills, explaining complicated concepts to non-technical colleagues. Generating insights from complex data sources. More ❯

We are very passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting edge unsupervised clustering algorithms, to novel applications of transformer neural networks. We test and measure the real customer impact of each model we deploy. What will I be doing? Identify areas where ML can create value for our customers More ❯

will research, implement and benchmark new machine learning algorithms to continuously improve your workflows. You will focus on generative models (flow models, diffusion and GANs), alongside neural network architectures that make use of non-classical feature sets. You'll be working in collaboration with clients and partners mapping relevant problems within hardware and deliver projects demonstrating how More ❯

will research, implement and benchmark new machine learning algorithms to continuously improve your workflows. You will focus on generative models (flow models, diffusion and GANs), alongside neural network architectures that make use of non-classical feature sets. You'll be working in collaboration with clients and partners mapping relevant problems within hardware and deliver projects demonstrating how More ❯

a hands-on, proactive role focused on continuous improvement across modelling, automation, and client-facing insights. You will work on: Enhancing ML pipelines: validation, classification, clustering, predictive models, neural networks, optimisation. Automating and streamlining data refresh processes. Producing client presentations and providing pricing insights. R&D on new modelling and optimisation approaches. Supporting business development and pitch materials. More ❯

large-scale production systems Experience building automated data pipelines Strong understanding of CI/CD pipelines and automation tools for efficient model deployment and monitoring Strong understanding of neural networks, CNNs, RNNs, LSTMs, and transformers Great knowledge of NLP algorithms Expertise in Python and PyTorch Desirable Skills 👌 Can speak, or learning to speak, more than one language Experience More ❯

LLMs) in practical operational settings, transforming research findings into functional business tools. Facilitate the progress and examination of ML models, involving both standard machine learning and neural network-focused models, as requested by R&D teams. Develop standardized, reusable workflows that can be applied across different projects and scientific areas. Collaborate with scientists and engineers to incorporate More ❯

Beneficial: Expertise in one or more of: AI/ML for imagery and remote sensing Reinforcement learning Natural Language Processing and Large Language Models Knowledge graphs and graph neural networks AI for RF, EW, radar, sonar, or acoustics Autonomy applications If you are interested and think you have the skills required as a Lead AI Technologist, please don More ❯

/MACHINE LEARNING/ML/NLP/NATURAL LANGUAGE PROCESSING/DEEP LEARNING/DATA SCIENCE/LSTM/LONG SHORT TERM MEMORY/CNN/COGNETIVE NEURAL NETWORKS/CLOUD INFRASTRUCTURE/ARTIFICIAL INTELLIGENCE/INNOVATION/SECURITY CLEARANCE/DV CLEARED/SC CLEARED/SC CLEARANCE/SECURITY CLEARANCE/DV CLEARANCE/DEVELOPPED More ❯

Installing and optimising Ubuntu (Canonical) across GPU and non-GPU compute nodes • Implementing and managing Kubernetes for container orchestration and performance at scale • Installing and configuring NVIDIA GPU Operator, Network Operator and Run:AI orchestration platform • Integrating Run:AI with Kubernetes clusters to deliver scalable GPU utilisation • Supporting deployment of HGX B200 GPU nodes (96 NVIDIA B200 GPUs) and … collaborative mindset Desirable Experience • Certifications in Kubernetes, NVIDIA or HPC infrastructure technologies • Experience in research, academic or scientific computing environments • Understanding of AI and ML workflows, neural network training and large language models • Familiarity with HPE, NVIDIA, Aarna and Digital Realty platforms If you are passionate about building and operating large-scale computing environments and want to More ❯