Machine Learning Intern
Raidium
Machine Learning Intern
Raidium
Self-supervised multimodal AI for radiology
Foundation models are pre-trained on large amounts of data and can be adapted to many downstream tasks with few or no labels. At Raidium, we build our own foundation models for radiology, for 3D CT and MRI imaging.
We released Curia, a state-of-the-art vision encoder for CT and MRI, trained on about 150,000 clinical exams. We then built Jolia, a vision-language model for 3D CT that learns from radiology reports at the level of individual findings. Our next steps include localization, multi-volume and longitudinal reasoning, and more interactive use of these models by radiologists.
As an intern, you join the research team and work on one research topic related to foundation model pre-training and adaptation. In particular, you will work development of generative VLMs with image grounding and localisation, with applications to findings classification and evolution of pathologies through time.
In this internship, you will:
Read the latest literature on foundation models research for vision and language
Design and run experiments on 3D vision and vision-language foundation models for radiology
Train models at scale on Raidium's dataset of radiology exams and reports, using GPU clusters
Benchmark your approach on clinical downstream tasks (detection, segmentation, classification, report generation)
Work with radiologists to define the needs and to check the results
Share your results with the team, and when possible, publish them in a top-tier conference or journal
You will have a daily follow-up with a researcher from the team, and a weekly meeting with senior researchers. The internship will be managed by a lead researcher at Raidium.
Raidium builds foundation models for radiology and the software that brings them to radiologists. Our models, Curia and Jolia, power a new generation of tools to read and analyze CT and MRI exams.
Raidium was founded by experts in radiology and machine learning:
Pierre Manceron, CEO, Centrale Paris engineer in Applied Mathematics and Machine Learning, former ML lead at Owkin
Paul Herent, CMO, radiologist and former interne des hôpitaux de Paris, Cogmaster (ENS) and PSL, with published research on deep learning in radiology
Final year of MSc in Machine Learning, Computer Science, Mathematics or Electrical Engineering (for example MVA), or PhD student looking for a research internship
Strong knowledge of Deep Learning, with Computer Vision and/or NLP
Good Python skills, and experience with PyTorch
Interest in research and in the healthcare domain
Authorization to work in France
Experience with Transformers, self-supervised learning or vision-language models
Experience with 3D or medical images
Experience with version control such as git
Experience with HPC clusters and multi-GPU training
Experience with coding agents such as Claude Code or Codex
Publications in top-tier Machine Learning or medical imaging venues
Work on real research problems with a direct impact on patient care
Train large models on a unique dataset of radiology exams and reports (130 TB)
Access to large GPU clusters, including European supercomputers
Work with a team of expert ML researchers, engineers and radiologists
Apply and improve the latest Deep Learning technologies to build an inspiring product
Have a real and positive impact on people's lives
Access to premium computing hardware
Competitive salary
Raidium provides an AI-powered radiology platform that helps healthcare providers improve the characterization of imaging biomarkers to support precision medicine. The system uses self-supervised learning on large unlabeled data and is multimodal, able to process text, images, and audio to perform multiple tasks across different imaging types. It is offered as a subscription service with regular updates and ongoing support, and it protects patient privacy by pseudonymizing data in compliance with GDPR. By focusing on unlabeled data learning and multimodal capabilities, Raidium aims to make radiology departments more accurate and efficient worldwide.
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