Role OverviewWe're seeking a
Research Scientist with deep expertise in
Computer Vision to join our ML 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
training and scaling vision encoders for radiology diagnosis across multiple modalities including X-rays, CT scans, and MRI. 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 learning frameworks.
- Evaluatemodel 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 contrastive learning, self-supervised learning, and foundation model pretraining.
- 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
- 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.)
