Machine Learning Scientist

at  Ground Truth Labs

GROUND TRUTH LABS

GTL is a spinout from the University of Oxford that will transform oncology through an AI-first approach to spatial biology.
We are building on our exceptional, interdisciplinary team to translate leading academic research into impact in healthcare. As an early team member, you’ll have the unique opportunity to help shape our journey and build on our principles of collaboration, focus, and invention.
We are on a mission to improve our understanding of disease and drive better patient outcomes through enhanced pathology. This is the new era of AI - you’ll be working on creating a novel type of biomarker that will improve the lives of millions of patients.

YOUR IMPACT

We are looking for a Machine Learning Scientist to play a critical role in the development of new spatial biology biomarkers for oncology.
You will develop algorithms that make use the of latest machine learning and computer vision techniques to analyse the cell and tissue morphology at different scales. We expect that you have an enthusiasm for AI products and a track record of interesting projects in image analysis to match.
This is a highly creative, fast-paced environment. Our teams are interdisciplinary, you will work alongside the engineering and clinical science to build robust algorithms and to develop methodologies that drive clinical adoption.
We are an early stage company, you will have the opportunity to drive the research direction, lead on new projects and make an impact across the organisation. The successful candidate will be able to identify, build, and champion algorithm products in key strategic areas for GTL.

• Design, develop and implement advanced deep-learning models to extract valuable insights from histology imaging data
• Collaborate with cross-functional teams, including engineers, clinical experts, and product managers, to identify opportunities for data-driven solutions and interpretable algorithms that address clinical questions
• Develop and maintain scalable data pipelines and infrastructure to support data science projects.
• Work closely with the Product, Software and Clinical and Regulatory teams to translate clinical-grade algorithms into tools suitable for software-as-a-medical-device
• Communicate results and insights to stakeholders and key decision-makers. As well as publicly in research publications and blog posts
• Stay up-to-date with the latest developments in the field of machine learning, medical imaging, and data science