AI Engineer, Medical Vision Language Models

Lumexa Imaging is one of the country's largest providers of outpatient medical imaging. With over 5,000 team members and more than 185 outpatient imaging centers across 13 states, our team conducts more than 4 million outpatient studies annually. We are the partner of choice for health systems and radiologists, delivering best-in-class clinical excellence, operations, and state-of-the-art technology across our platform.

Lumexa Imaging is hiring an early-career AI Engineer to help build, adapt, and deploy vision language models (VLMs) and imaging foundation models for clinical radiology. You will work hands-on using agentic coding tools (Claude Code, OpenAI Codex) to transition ideas from radiologists into validated, deployed models. This is a build-focused role centered on model development and deployment.

Initial priorities include building clinical LLM pipelines (prior report summarization, scanned document summarization, and relevant prior selection) on infrastructure shared with our VLM work.

Why this role is a rare opportunity

You will have access to a large, growing repository of clinical imaging and reporting data. A central part of this job is leveraging this real-world, domain-specific data, consistent with applicable privacy, security, and governance requirements, to develop, adapt, and evaluate AI models beyond what is possible using public data alone.

You will work closely with the Chief Clinical AI Officer, the Clinical Applications team, and Infrastructure and Information Security.

Essential Duties and Responsibilities:

• VLM Development and Adaptation: Fine-tune and adapt open VLMs (e.g., Qwen-VL, LLaVA-Med, MedGemma, InternVL) and imaging foundation models for report drafting, impression generation, finding detection, and prior comparison. Apply parameter-efficient methods (LoRA, QLoRA) and full fine-tuning under senior guidance.
• Vision Encoders and Representation Learning: Work with vision encoders, contrastive image-text models (CLIP, SigLIP), and self-supervised approaches (JEPA). Build and maintain embedding pipelines for retrieval, cohort discovery, case similarity search, and drift detection
• Data Curation: Curate and version datasets from our internal PACS and reporting systems, including DICOM header, burned-in pixel, and paired-report de-identification
• Agentic Engineering: Use Claude Code and OpenAI Codex daily to build, test, and maintain code. Maintain project context (CLAUDE.md, AGENTS.md), and help build and run MCP servers for internal system access
• Deployment and MLOps: Deploy models via containerized inference and GPU serving (vLLM, Triton), support CI/CD pipelines, and monitor production models for drift and performance
• Evaluation: Build automated evaluation harnesses for clinical accuracy, negation, laterality, and hallucination scoring against radiologist ground truth
• Workflow Automation: Build agentic and LLM driven tools for operational workflows, such as converting unstructured requests into RIS scheduling actions and supporting intelligent operations dashboards

Required Qualifications:

• Hands-on experience fine-tuning transformer models in PyTorch (vision, multimodal, or language) using Hugging Face, MONAI, or similar (via internships, research, open-source work, or projects)
• Working knowledge of embeddings and vector search
• Experience with open VLMs such as Qwen-VL or LLaVA
• Daily use of agentic coding tools (Claude Code, OpenAI Codex)
• Familiarity with Docker and GPU-based model serving
• Comfort working with real-world data and a reproducible approach to experiments

Preferred Qualifications:

• Hands-on experience with vision encoders and contrastive models (CLIP, SigLIP, JEPA)
• Exposure to medical VLMs, imaging foundation models, or self-supervised methods (DINOv2, MAE)
• Experience building MCP servers in Python or TypeScript
• Familiarity with medical imaging formats (DICOM, HL7, FHIR) and HIPAA/PHI handling
• Awareness of MLOps tooling (MLflow, Weights & Biases, model registries)
• Awareness of FDA considerations for clinical AI (510(k), PCCP)

Education/Licensing/Certification:

Degree in Computer Science, Biomedical Engineering, or a related field (or equivalent practical experience)

Experience (years and type):

Experience 0 to 3 years (internships, research, or projects count)

Travel Required (% of time): Minimal, up to 10%

Physical Requirements &

• Work Environment: Standard remote office work Lumexa Imaging provides a competitive compensation program to attract, retain, and motivate a high-performance workforce.
• Lumexa Imaging is an equal opportunity employer.