AI Researcher

Responsibilities

• 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.
• 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, Tensor Flow, 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, Num Py, SQL; familiarity with Spark or similar is a plus.
• Model…