Research Scientist - Vision Foundation Models

ABOUT US

We're tackling one of healthcare's most critical challenges in medical imaging and diagnostics. Our company operates at the intersection of cutting-edge AI and clinical practice, building technology that directly impacts patient outcomes. We've assembled one of the industry's most comprehensive and diverse medical imaging datasets and have a proven product-market fit with a substantial customer pipeline already in place.

ROLE OVERVIEW

We're seeking a Research Scientist with deep expertise in vision foundation models to join our ML Research team. You'll be at the forefront of developing and deploying state-of-the-art vision models for medical imaging applications. This role focuses on pretraining and scaling vision encoders for radiology diagnosis across X-ray, CT, and MRI, with a growing emphasis on 3D volumetric modeling. You'll work with one of the largest and most diverse medical imaging datasets in the industry, pushing the boundaries of what's possible in AI-assisted diagnosis while maintaining the rigor required for clinical deployment.

KEY RESPONSIBILITIES

- Design, train, and scale vision foundation models for radiology applications across X-ray, CT, and MRI modalities, implementing self-supervised, contrastive, masked image modeling, and joint-embedding predictive (JEPA) frameworks.

- Extend 2D pretraining recipes to volumetric CT and MR data, addressing long sequence lengths, anisotropic spacing, and multi-sequence studies.

- Evaluate model performance rigorously across academic benchmarks, internal offline datasets, and live production data.

- Contribute hands-on to all stages of model development including dataset curation, architecture design, distributed training, and production deployment.

- Stay current with cutting-edge research in computer vision and medical imaging AI.

- Drive research and technical excellence through conference publications and technical blog posts, establishing best practices for training robust medical imaging models at scale.

QUALIFICATIONS

- 6+ years of academia/industry experience in computer vision/machine learning

- Deep expertise in training vision encoder models at scale (e.g. ViT, ConvNeXt). Strong foundation in self-supervised pretraining, including contrastive, masked image modeling, self-distillation, and JEPA-style objectives.

- Experience training on volumetric or spatiotemporal data (video, 3D medical imaging)

- Track record of implementing complex models from research papers and adapting them to new domains

- Proficiency in PyTorch or JAX, with experience training models on multi-GPU/distributed systems

- Hands-on experience with medical imaging applications, particularly radiology (X-ray, CT, MRI)

- Strong software engineering skills and ability to write production-quality code

PREFERRED QUALIFICATIONS

- Publications at top-tier conferences (CVPR, ICCV/ECCV, NeurIPS, ICLR, MICCAI)

- Experience with 3D medical image processing and retrieval tasks

- Familiarity with CT and MR acquisition (windowing, multi-sequence protocols, voxel spacing)

- Experience with long-context training techniques (sequence parallelism, efficient attention)

- Knowledge of vision-language models and multimodal learning

- Experience with model interpretability and explainability methods

- Understanding of clinical evaluation metrics, clinical workflows, and healthcare data (DICOM, HL7, etc.)