We seek an experienced AI Research Scientist to lead fundamental and applied research in machine learning and artificial intelligence. The successful candidate will innovate model architectures, publish in top-tier venues, and work with engineering teams to translate research into production. This role suits someone who combines deep theoretical understanding with practical engineering skills and who thrives in a collaborative, fast-paced research centre.
AI Research Scientist Job Profile
The AI Research Scientist will design, implement and evaluate novel algorithms across areas such as deep learning, natural language processing, computer vision and reinforcement learning. You will lead experimental programmes, mentor junior researchers and coordinate with product teams to ensure research impact.
This position demands a strong publication record, excellent programming skills, and the ability to communicate complex ideas clearly to scientists and engineers. You will contribute to the organisation's strategic research roadmap and help define research priorities.
AI Research Scientist Job Description
The AI Research Scientist is responsible for advancing the state of the art through rigorous experimentation, theoretical analysis and prototype development. You will identify promising research directions, design reproducible experiments, and develop models that are robust and efficient for deployment. Collaboration is central: you will partner with data engineers, software developers and product managers to move research from the lab to production systems.
Core activities include conceiving novel model architectures, improving training and optimisation procedures, and investigating model interpretability and fairness. The role also includes writing technical reports, submitting papers to conferences, and presenting findings to internal and external stakeholders. You will act as a technical leader, mentoring junior researchers and contributing to hiring and research centre activities.
In addition to research, you will participate in setting research strategy, evaluating external partnerships, and ensuring reproducibility of experiments. A pragmatic approach to engineering trade-offs and a focus on real-world impact will be essential, as will adherence to ethical guidelines and best practice in data governance.
AI Research Scientist Duties and Responsibilities
- Lead and execute research projects in machine learning and AI with measurable outcomes.
- Design, implement and evaluate novel algorithms and model architectures.
- Develop reproducible experimental pipelines and maintain research codebases.
- Publish research in peer-reviewed conferences and present at workshops.
- Collaborate with engineering teams to productionise models and prototypes.
- Mentor junior researchers and interns, and contribute to team building.
- Perform model validation, testing, and rigorous statistical analysis.
- Ensure ethical considerations, fairness and robustness in model development.
- Contribute to research strategy, roadmaps and grant or partnership proposals.
- Engage with the research community and represent the organisation externally.
AI Research Scientist Requirements and Qualifications
- PhD in Computer Science, Mathematics, Statistics, Physics or related field, or equivalent research experience.
- Strong publication record in machine learning, NLP, computer vision or related areas.
- Proven experience with deep learning frameworks such as PyTorch or TensorFlow.
- Expertise in model training, evaluation, optimisation and scalable experimentation.
- Proficient in Python and familiar with software engineering best practices.
- Experience deploying models to production environments and working with cloud platforms.
- Excellent communication skills and the ability to explain complex ideas to diverse audiences.
- Ability to work collaboratively in cross-functional teams and to mentor colleagues.
- Knowledge of ethical AI principles, data governance and reproducibility standards.
- Desirable: experience with large language models, generative models, or reinforcement learning.
