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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Researcher - **Company:** Thales Group - **Location:** Reading, UK - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Artificial Neural Networks, Computer Vision, Big Data, Software Quality, Cyber Security, Nvidia CUDA, Data Cleansing, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, OpenCV, Tensorflow, Software Deployment, Software Engineering, SQL Databases, Reinforcement Learning, Data Processing, High Performance Computing, Feature Engineering, Pytorch, Large Language Models, Apache Spark, Deep Learning, Model Validation, Fastapi, Pandas, Containerization, AI Platforms, Scikit Learn, Information Technology, ONNX (Open Neural Network Exchange) Format, HuggingFace, Machine Learning Operations, Grpc, Unsupervised Learning, Data Generation - **Published:** August 11, 2026 - **Apply:** https://thales.wd3.myworkdayjobs.com/Careers/job/Reading/AI-Researcher_R0336386 ## About the Role Degree/Masters, an equivalent in a relevant Software/AI subject, or equivalent experience. Relevant subject areas may include Artificial Intelligence, Machine Learning, Computer Science, Data Science, Mathematics, Engineering, Physics or a related technical discipline. * Strong Python programming skills; proficiency with modern software engineering and research practices, including testing, code quality, reproducibility and collaborative development. * Experience conducting AI/ML research or advanced AI development in complex technical environments, preferably including defence, aviation, rail, cyber security, safety-critical, mission-critical or similarly regulated domains. * Expertise in ML/DL algorithms and techniques for supervised, unsupervised, self-supervised and, where relevant, reinforcement learning. * Proven ability to take AI research from problem framing through experimental design, model development, evaluation and prototype demonstration. * Hands-on experience in at least one advanced AI area such as deep neural networks, computer vision, NLP/LLMs, multimodal AI, self-supervised learning, reinforcement learning, time-series analytics or foundation models. * Experience with AI frameworks and libraries: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers; OpenCV for vision applications. * Strong understanding of experimental design, statistical evaluation, benchmarking and model validation. * Experiment tracking and reproducibility tools, for example MLflow, Weights & Biases or equivalent. * Data wrangling and analysis using Pandas, NumPy, SQL; familiarity with Spark or similar is a plus. * Model optimisation and research-to-prototype deployment fundamentals, including ONNX, TorchScript, FastAPI/gRPC and GPU acceleration, with CUDA basics desirable. * Responsible AI and security awareness: explainability, privacy-preserving methods, bias assessment, safety, assurance, adversarial robustness and secure AI. * Scientific and technical writing skills, including preparation of technical reports, research papers, invention disclosures, model cards and experiment reports. * Demonstrable experience producing high-quality technical documentation, research outputs, model evaluations or stakeholder briefings. Interpersonal Skills * Ability to engage and influence diverse stakeholders, including Product Engineering Leaders, Customers, Design Authorities, Project Management, IS/IT, research partners and academic collaborators. * Highly effective in a matrix-based organisation; a collaborative team player who drives outcomes while maintaining scientific rigour. * Excellent communication skills; able to explain complex AI research concepts clearly to technical and non-technical audiences. * Curious, creative and intellectually rigorous, with the ability to challenge constructively and develop novel solutions to ambiguous problems. * Encourages an open environment where ideas are shared, technical debate is welcomed and innovation thrives. Desirable * PhD or equivalent research experience in Artificial Intelligence, Machine Learning, Computer Science, Mathematics, Engineering, Physics or a related discipline. * Peer-reviewed publications, patents, invention disclosures, open-source contributions or recognised technical innovations. * Experience with governance of architecture, research designs or detailed technical designs throughout the project lifecycle. * Experience with large-scale data initiatives, data labelling strategies, synthetic data generation and data quality management. * Familiarity with MLOps practices and cloud platforms for AI deployment. * Experience working with academic partners, research institutions, grant-funded programmes or collaborative research consortia. * Experience contributing to bids, proposals, customer demonstrations or externally funded innovation activities. * Knowledge of cloud AI services, HPC environments, containerisation and secure research environments is desirable. ## Description * Conduct applied and experimental AI research to solve complex customer and business problems across defence, aerospace, cyber security, rail, critical national infrastructure and related domains. * Develop state-of-the-art AI/ML solutions, proofs of concept and research prototypes using real-world data and operationally relevant problem statements. * Investigate, design, implement and evaluate advanced AI methods, including but not limited to deep learning, multimodal AI, self-supervised learning, foundation models, generative AI, computer vision, NLP/LLMs, time-series analytics and reinforcement learning where relevant. * Translate business and customer needs into clear research questions, experimental plans, technical requirements and measurable success criteria. * Design robust evaluation methodologies, including baselines, benchmarks, ablation studies, uncertainty assessment, robustness testing and performance measurement against operationally meaningful metrics. * Collaborate with AI V&V, AI Assurance, Human-Machine Teaming and Applied AI groups to ensure research outputs are trustworthy, human-centred, explainable and suitable for future operational use. * Build reproducible research pipelines, including data preprocessing, feature engineering, model training, experiment tracking, evaluation and technical reporting. * Ensure Responsible AI practices are embedded throughout the research lifecycle, including robustness, safety, explainability, transparency, fairness, privacy, security and alignment with MOD, regulatory and Thales governance requirements. * Create proofs of concept, publications, invention disclosures, patents, technical reports and reusable research assets around advanced AI topics such as multimodal learning, self-supervised learning, foundation models and human-AI collaboration. * Package research outputs in a form that enables transition to AI engineering teams, including demonstrator code, model cards, experiment reports, design notes and handover documentation. * Support bids, PoCs, demos, customer workshops, innovation campaigns and stakeholder briefings by communicating research concepts and outcomes to technical and non-technical audiences. * Work with data engineers, architects and domain experts on data acquisition, labelling strategies, synthetic data approaches, integration of third-party data and data quality management. * Horizon scan for major AI research and technology trends, assess relevance to Thales markets, run trials and share best practices to accelerate responsible adoption. ## Related Videos - [Exploring the Power of gRPC-Gateway for Writing RESTful Services](https://www.wearedevelopers.com/videos/2072-exploring-the-power-of-grpc-gateway-for-writing-restful-services) - [Vectorize all the things! 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