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About the Role

We are seeking a Research Scientist with deep expertise in large-scale vision-language pretraining to join our ML Research team. You will be at the forefront of developing state-of-the-art multimodal models for clinical use in radiology settings. This role owns the pretraining stage of our radiology report generation model, focusing on VLM architecture design, multimodal data and task mixtures, and large-scale training runs.

Responsibilities

Design, train, and scale vision-language foundation models for radiology applications, owning the pretraining stage end to end.
Develop VLM architectures suited to medical imaging, including native and variable resolution handling, high-resolution tiling, connector design, and token budgets for volumetric studies.
Build and tune multimodal pretraining mixtures across captioning, VQA, grounding, and retrieval tasks, balancing data sources to avoid regressions in language capability.
Develop fine-grained visual grounding during pretraining, enabling models to localize findings within medical images using bounding boxes or segmentation masks.
Own pretraining evaluation (zero- and few-shot transfer, probing, and downstream fine-tunability) as the signal for base model quality.
Train joint vision-language embedding spaces using contrastive and generative objectives, including region- and sentence-level alignment between images and reports.
Contribute hands-on to all stages of pretraining including dataset curation, architecture design, distributed training, and handoff of base checkpoints to post-training.
Stay current with cutting-edge research in vision-language modeling and large-scale multimodal pretraining.
Drive research and technical excellence through conference publications and technical blog posts, establishing best practices for pretraining medical VLMs at scale.

Requirements

6+ years of academia/industry experience in vision-language modeling, multimodal learning, or related fields
Deep expertise in pretraining large vision-language models (e.g., LLaVA, Flamingo, CogVLM, Qwen-VL, InternVL, or similar architectures)
Strong foundation in modern VLM pretraining techniques including: Vision-language connector and fusion architectures (projection, cross-attention, resampler-based)
Variable and high-resolution image handling (native resolution, dynamic tiling, token compression)
Contrastive and generative objectives for learning joint vision-language embedding spaces
Data and task mixture design, including curriculum and mixture-ratio ablations
Experience with fine-grained visual grounding (referring expression comprehension, phrase grounding, box or mask prediction)
Track record of implementing complex models from research papers and adapting them to new domains
Proficiency in PyTorch or JAX, with experience training large models on multi-GPU/distributed systems
Experience with autoregressive language modeling and long-context training
Hands-on experience with medical imaging applications, particularly radiology report generation
Strong software engineering skills and ability to write production-quality code
Publications at top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, MICCAI)
Experience training vision encoders from scratch, or co-designing them with a downstream VLM
Experience with interleaved image-text pretraining and synthetic recaptioning pipelines
Experience with 3D medical image processing and temporal modeling
Familiarity with clinical NLP and medical knowledge representation
Knowledge of evaluation methodologies for long-form generation, including factuality assessment and hallucination detection
Experience with model interpretability, explainability, and uncertainty quantification in safety-critical applications

Skills

Large-scale vision-language pretraining
Multimodal models
Radiology report generation
VLM architecture design
Multimodal data and task mixtures
Large-scale training runs
Medical imaging datasets
Clinical rigor
Vision-language foundation models
Medical imaging
Native and variable resolution handling
High-resolution tiling
Connector design
Token budgets for volumetric studies
Captioning
VQA
Grounding
Retrieval tasks
Fine-grained visual grounding
Bounding boxes
Segmentation masks
Pretraining evaluation
Zero-shot transfer
Few-shot transfer
Probing
Downstream fine-tunability
Joint vision-language embedding spaces
Contrastive objectives
Generative objectives
Region-level alignment
Sentence-level alignment
Dataset curation
Distributed training
Base checkpoints
Vision-language modeling
Large-scale multimodal pretraining
Conference publications
Technical blog posts
Pretraining medical VLMs
PyTorch
JAX
Multi-GPU systems
Distributed systems
Autoregressive language modeling
Long-context training
Medical imaging applications
Radiology report generation
Software engineering
Production-quality code
Vision encoders
Interleaved image-text pretraining
Synthetic recaptioning pipelines
3D medical image processing
Temporal modeling
Clinical NLP
Medical knowledge representation
Long-form generation evaluation
Factuality assessment
Hallucination detection
Model interpretability
Model explainability
Uncertainty quantification

Experience Level

6+ years of academia/industry experience

About the Company

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.