Machine Learning Scientist

We’re Aioi R&D Lab – an AI tech hub in one of the fastest-growing insurance companies. We research and develop AI systems that catapult insurance from a slow-moving, traditional past into a data-driven, technology-lead and society-defining future.

We’re looking for dynamic, driven professionals like you to help evolve our business in new directions. You’ll need first-class credentials and a proactive attitude to help us drive the change the underpins our mission.

As a Machine Learning Scientist, you will 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.

If you’d like to be part of our brighter future, and share in our success, we’d love to hear from you.

BE THE NEXT BIG THING, NOW.

Responsibilities

* Design, develop, test and maintain machine learning and AI software in support of the CODAS project and related R&D Lab initiatives.

* Contribute to the delivery of technical components for a sovereign AI ecosystem, including ML services, data processing pipelines, evaluation tooling and deployment-ready applications.

* Support the translation of research outputs into robust, maintainable and operationally viable systems.

* Work with internal colleagues and external project partners to understand technical requirements, constraints and delivery priorities.

* Help adapt AI and ML approaches to privacy-sensitive, security-conscious and regulated deployment contexts.

* Contribute to the integration of technical solutions into project infrastructure, workflows and partner environments.

* Troubleshoot technical, data and integration issues arising during development and deployment.

* Support the development and improvement of internal tooling, engineering standards, reusable frameworks and deployment practices across the Lab.

* Communicate technical trade-offs, risks and progress clearly to both technical and non-technical stakeholders.

Knowledge, Experience and Qualifications

Essential

* A Master’s degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a closely related discipline, or equivalent research or industry experience.

* Established 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.

* Software engineering foundations in Python, including ML frameworks, version control, CI/CD, and reproducible experimental practices.

* Skills handling challenging data that requires understanding, preparation, oraganisation 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.

* Communication skills, with the ability to explain technical concepts and trade-offs clearly to both technical and non-technical audiences.

* Ability to work effectively in technically complex and ambiguous environments…