Modeling [h1.location_city]

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

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