Senior Machine Learning Scientist

The role will support the development of a privacy-preserving generative AI ecosystem designed to protect personal and sensitive corporate data by design, including data residency, anonymisation and secure deployment protocols and principles, as well as model-based data protection achieved during both training and inference time. Working within a multidisciplinary and multi-partner environment, the postholder will research technical concepts core to the program and contribute towards developing them into dependable systems and components that can be used in live settings, while also supporting the broader technical development of the Lab and its collaboration with CODAS and other project partners.

Additionally, the postholder will create publishable material based on the work carried out in the program, and attend events such as conferences and workshops to disseminate the progress made.

• Lead or substantially contribute to technical research work streams within assigned CODAS tracks, setting experimental direction and ensuring delivery of high-quality outputs within programme timelines.
• Design, develop and validate machine learning models, algorithms and approaches for privacy-preserving generative AI, including areas such as differential privacy, federated learning, and secure inference.
• Serve as a technical research lead on assigned work streams; design novel approaches or adapt existing methods to meet programme objectives beyond the current state of the art.
• Contribute to the translation of research outcomes into proof-of-concept systems, toolkits and deployable components that form part of the programme deliverables.
• Work with internal colleagues and external project partners to understand technical requirements, constraints and delivery priorities.
• Mentor and support junior colleagues (ML engineers and research associates), scoping and delegating tasks aligned to their development and the needs of the programme.
• Contribute to scientific publications, technical reports, conference presentations and other dissemination activities in support of the programme’s commitment to advancing the field.
• Champion best practices in ML research, software design, experimental rigour, reproducibility and responsible AI across the team.
• Communicate complex technical findings and trade‑offs clearly to both technical and non-technical stakeholders, including partner organisations and programme leadership.

• A Master’s degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a closely related discipline, or equivalent research or industry experience.
• 5+ year work experience in relevant field.
• A strong and demonstrable track record in applied ML/AI research or development, with recognised expertise in one or more relevant areas such as privacy-preserving ML, generative AI, large language models, or federated learning.
• Deep understanding of modern ML methods, their assumptions and limitations, and the ability to reason about appropriate application in novel or constrained settings.
• Strong software engineering foundations in Python, including ML frameworks, version control, CI/CD, and reproducible experimental practices.
• Strong skills handling challenging data that requires understanding, preparation, organisations and augmentation before use for the purposes of ML/AI research or development.
• Experience working across the full ML lifecycle — from problem framing, experimentation and prototyping through to evaluation, integration and deployment.
• Ability to contribute to scientific publications, technical reports, and the dissemination of research findings to a wide audience.
• Proven ability to mentor or support junior colleagues and scope technical work aligned to team and programme goals.
• Strong communication skills, with the ability to explain complex technical concepts and trade‑offs clearly to both technical and non-technical audiences.
• Ability to work effectively in technically complex and ambiguous environments involving multiple stakeholders.

• Experience with privacy-preserving ML techniques such as differential privacy, federated learning,…