Research Scientist - Vision-Language Modeling

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 Language Modeling (VLMs) to join our ML team. You'll be at the forefront of developing and deploying state-of-the-art multimodal models for clinical use in radiology settings. This role focuses on training and fine-tuning vision-language models (VLMs) that can generate accurate & grounded radiology reports across multiple imaging 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, advancing the state-of-the-art in grounded medical report generation, model alignment, and inference-time reasoning while maintaining the clinical rigor required for healthcare deployment.

Key Responsibilities

• Design, train, and scale vision-language foundation models for radiology applications.

• Develop and implement advanced post-training strategies including preference optimization (DPO, IPO, KTO), reinforcement learning from human feedback (RLHF), and other alignment techniques to improve clinical accuracy and reduce hallucinations.

• Research and deploy inference-time compute scaling techniques such as chain-of-thought reasoning, self-refinement, and test-time training to enhance model performance on complex diagnostic cases.

• Pioneer grounded report generation capabilities, enabling models to spatially localize findings within medical images using bounding boxes or segmentation masks.

• Design rigorous evaluation frameworks that assess text for medical accuracy and writing style.

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

• Stay current with cutting-edge research in vision-language modeling, medical AI, and model alignment techniques.

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

Qualifications

• 6+ years of academia/industry experience in vision-language modeling, multimodal learning, or related fields

• Deep expertise in training and fine-tuning large vision-language models (e.g., LLaVA, Flamingo, CogVLM, Qwen-VL, or similar architectures)

• Strong foundation in modern post-training techniques including:

  • Preference optimization methods (DPO, IPO, ORPO, KTO)

  • RLHF and reward modeling

  • Inference-time compute scaling and reasoning strategies

  • Constitutional AI and other alignment techniques

• 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 instruction tuning

• Hands-on experience with medical imaging applications, particularly radiology report generation

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

Preferred Qualifications

• Publications at top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, MICCAI)

• Experience with grounded generation tasks (visual grounding, referring expression comprehension)

• Knowledge of evaluation methodologies for long-form generation, including factuality assessment and hallucination detection

• Experience with 3D medical image processing and temporal modeling

• Familiarity with clinical NLP and medical knowledge representation

• Experience with model interpretability, explainability, and uncertainty quantification in safety-critical applications