Machine Learning Machine Learning Scientist Remote

Ground Truth Labs

GTL is a well-funded, early-stage startup developing state-of-the-art digital technologies to better understand, diagnose and treat blood cancer patients.

We have built an exceptional, small-by-design, interdisciplinary team to translate leading academic research into real impact on global health.

Since 2023 we have made considerable progress in developing our technology, deploying our first generation of algorithms commercially with global biopharma companies, and working with world-leading academic medical centres to continue developing and validating new biomarker tools that improve patient outcomes. As a member of our small team, you will have the opportunity to help shape our journey and build on our principles of collaboration, focus, and invention.

We are looking for a Machine Learning Scientist to play a critical role in the development of new spatial biology biomarkers for haemato-oncology.

You will develop algorithms that make use of the 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 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.

• Manage product, software and clinical and regulatory inputs and requirements to translate research algorithms into clinical-grade tools suitable for use in the real world.

• Communicate results and insights to stakeholders and key decision-makers. As well as publicly in research publications and and conference abstracts.

• Stay up-to-date with the latest developments in the field of machine learning, medical imaging, and data science

• PhD or equivalent practical experience in a technical field

• Experience and a track record of innovation in deep learning and statistical analyses

• Expertise in processing and analysing large image datasets using statistical methods

• Ability to work independently and as part of multidisciplinary team within in a fast-paced startup environment

• Strong communication and collaboration skills. Both remote (excellent written communication is key) and in-person

• Strong programming skills in Python and experience with Py Torch

• Track record in computational pathology and/or single-cell transcriptomics analysis, sequencing

• Clinical background or knowledge of cancer biology

• Expertise in multimodality integration which includes histology data, clinical data, sequencing or other multi-omics

• Relevant research experience to the position such as post doctoral roles, a proven track record of publications, or contributions to machine learning codebases

This is an opportunity to join a small, high-calibre team and take ownership of foundational engineering work at a pivotal stage of company growth.

You will work alongside talented machine learning scientists and engineers to build the platform that supports our research, partner delivery, and future products. The role is especially well suited to someone who enjoys building from the ground up, moving quickly, and having tangible impact across an organisation and field.

We primarily work remotely with a strong emphasis on efficient processes that enable high-quality work across asynchronous teams. At the same time, we recognise the importance of spending time together in person to share knowledge, solve hard problems, and build strong working relationships. We therefore expect this role to involve regular travel to London and Oxford. Indicatively, a typical month may involve 2-8 in-person days.

As a team, we value: