AI/ML Scientist

AI/ML Scientist

We're hiring an AI/ML Scientist to design, build, and ship machine learning that makes it into real products, not just papers. You'll sit at the intersection of applied research and engineering: framing hard problems, prototyping models across areas like deep learning, computer vision, and image/video analysis, and taking the promising ones all the way to production and measurable impact. Suits someone with a strong research foundation who is energised by seeing their work used in the real world.

Minimum Qualifications

• Education/Experience: PhD or Master's degree in Computer Science, Machine Learning, Computer Vision, Biomedical/Electrical Engineering, or a related field, or equivalent industry research experience with a demonstrable track record of shipped ML work.
• ML Depth: Deep expertise in modern machine learning and deep learning, with hands-on strength in at least one domain such as computer vision, image/video analysis, or signal/sensor data.
• Programming: Strong programming skills in Python and fluency with a deep learning framework (PyTorch and/or TensorFlow).
• Research to Production: Proven ability to take a model from research prototype to production, including sound experimental design and honest evaluation.
• Fundamentals: Solid grounding in statistics, linear algebra, and optimisation, with the ability to reason about why a method works or fails.
• Communication: Excellent communication skills - able to explain complex ideas simply and collaborate across research, engineering, and product.
• Track Record: History of research output - peer-reviewed publications, patents, open-source contributions, or comparable applied results.

Preferred Qualifications

• Experience leading or mentoring a team - line-managing scientists/engineers, setting technical direction, or growing a research function. A strong plus, not a requirement.
• Experience deploying models into production environments, including MLOps practices (CI/CD for models, monitoring, reproducibility) and, where relevant, inference on constrained or edge devices.
• Familiarity with privacy-preserving or federated learning, or working with sensitive/regulated data.
• Experience adapting ML across more than one application domain (e.g. healthcare, sports, industrial, media), showing an ability to transfer methods to new problems.
• History of collaborating with academic or clinical/industry partners on applied research.
• Background in healthcare, life sciences, or another regulated, data-sensitive domain (HIPAA, GDPR, ISO 270001, medical-device software).

About the Role

The AI/ML Scientist designs, builds, and ships machine learning that makes it into real products, not just papers. This role sits at the intersection of applied research and engineering: framing hard problems, prototyping models, and taking the promising ones all the way to production and measurable impact. It suits someone with a strong research foundation who is energised by seeing their work used in the real world, partnering closely with engineers, product managers, and domain experts to turn ambiguous questions into deployed systems.

Key Responsibilities

• Applied Research:
  • Lead applied research projects end to end - from problem definition and literature review through experimentation, evaluation, and deployment.
  • Design and train models across areas such as deep learning, computer vision, and image/video analysis, adapting state-of-the-art techniques to our data and domain.
  • Stay current with the field, run experiments on emerging methods, and share knowledge across the team through reviews, talks, and documentation.
• Production Delivery:
  • Translate research prototypes into robust, production-ready systems in collaboration with ML and platform engineers.
  • Define rigorous evaluation methodology - metrics, benchmarks, and validation that hold up under real-world conditions and edge cases.
• Collaboration and Direction:
  • Work cross-functionally to identify where ML can move the needle, and communicate findings clearly to both technical and non-technical stakeholders.
  • Contribute to the team's technical direction, standards, and best practices.

Success Metrics

• Applied research shipped to production and the measurable impact it creates for users.
• Rigour and reliability of evaluation methodology, including performance under real-world edge cases.
• Quality and cadence of research output and knowledge-sharing across the team.
• Effectiveness of cross-functional collaboration in identifying and prioritising high-value ML opportunities.

Core Competencies

• Strong research foundation paired with a bias toward shipping - research that ends in production, not a drawer.
• Sound judgement on experimental design and honest evaluation, including knowing when a method is failing and why.
• Excellent written and verbal communication across research, engineering, and product audiences.
• Autonomy to shape problems and approaches, with the ability to grow toward technical or people leadership.