Senior Machine Learning Engineer

Hypervision Surgical(“Hypervision”)is a spin-out from King’s College London, founded by clinicians and experts in medical imaging and artificial intelligence. Using safe light alone, our mission is to equip surgeons with real-time, AI-driven tissue intelligence to improve precision and patient safety.

We are pioneering the world’s first regulatory-cleared real-time intraoperative spectral imaging platform, combining on-chip spectral sensing with high-speed AI analytics at over 60 frames per second. Seamlessly integrating into existing surgical vision systems, our technology transforms standard cameras into intelligent, data-rich tools, revealing anatomical, physiological, and pathological information beyond human vision.

Certified for both open and minimally invasive surgery, our platform achievedUKCA certificationandFDA clearance in 2025 under a newly established AI/ML product code, andwas admitted into the FDA’s Safer Technology Program. With multi-centre clinical evaluations underway and strategic partnerships with world-leading technology and surgical manufactures,includingimecandZEISS Ventures,Hypervision is shaping the future of data-driven surgery.

Hypervision Surgical process all personal data in accordance with the UK GDPR and Data Protection Act 2018. For further information on how we collect, use and protect your data, please refer to our Applicant Privacy Notice.

We are seeking an experienced Senior Machine Learning Engineer to support the development and deployment of our AI/ML surgical vision platform, taking algorithms from research prototype to production deployment, and building the data pipelines that turn our hyperspectral imaging system into a continuously improving clinical tool.

As a Senior Machine Learning Engineer, you will work alongside our research scientists, engineers, and clinical development team to shape both the algorithms and the platform that delivers them. In particular, you will

• contribute architecturally and in a hands-on capacity to the design, training, evaluation, and production deployment of machine learning models for hyperspectral image processing, including image reconstruction, tissue characterisation, and semantic segmentation
• design and build the data pipelines that turn raw clinical recordings into structured, governed training datasets, supporting continuous training, model improvement, and re-validation cycles
• architect and operate the production ML stack, including model versioning, deployment, monitoring, drift detection, and rollback, for our cloud-enabled, regulatory-cleared surgical imaging platform
• establish and maintain MLOps best practices, including reproducible training, dataset governance, experiment tracking, model documentation, that scale across multiple algorithms, sensors, and clinical indications
• mentor more junior research scientists and engineers
• identify and surface novel features in support of patenting activities
• work closely with our software development and regulatory team for efficient integration from R&D to deployment

At Hypervision Surgical, we welcome candidates who have the core skills for the post and are keen to learn and grow with us. We are committed to creating an inclusive environment where a diverse mix of talented people come and enjoy working with each other. By working together, we will change the way surgery is performed and improve patient care.

• PhD or MSc in Machine Learning, Physics, Mathematics, Computer Vision, or related technical discipline
• 6+ years industry experience designing, training, evaluating, and deploying machine learning models, ideally for vision applications in a regulated medical context
• Demonstrated track record of taking ML systems from research prototype to production deployment at scale
• Strong experience building and maintaining data pipelines for continuous training, with a focus on reproducibility, dataset versioning, and efficient access
• Working knowledge of cloud platforms (AWS, GCP, or Azure) and modern MLOps tooling (e.g. MLflow, Weights & Biases, DVC, Airflow)
• Strong experience with Python and associated scientific software packages such as PyTorch, OpenCV, Pandas, Sci…