AI Engineer, Medical Vision Language Models
About The Position
Lumexa Imaging is hiring an early-career AI Engineer to build, adapt, and deploy vision language models (VLMs) and imaging foundation models for clinical radiology. This build-focused role uses agentic coding tools, including Claude Code and OpenAI Codex, to move radiologist ideas into validated, deployed models. Initial priorities include clinical LLM pipelines for prior report summarization, scanned document summarization, and relevant prior selection, using infrastructure shared with VLM work. The role offers access to a large, growing repository of clinical imaging and reporting data. The engineer will use this domain-specific data, subject to applicable privacy, security, and governance requirements, to develop, adapt, and evaluate AI models. The position works closely with the Chief Clinical AI Officer, the Clinical Applications team, and Infrastructure and Information Security.
Requirements
- Hands-on experience fine-tuning transformer models in PyTorch for vision, multimodal, or language tasks using Hugging Face, MONAI, or similar; experience may come from 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 such as Claude Code or OpenAI Codex.
- Familiarity with Docker and GPU-based model serving.
- Comfort working with real-world data and a reproducible approach to experiments.
- Degree in Computer Science, Biomedical Engineering, or a related field, or equivalent practical experience.
- 0 to 3 years of experience; internships, research, or projects count.
Nice To Haves
- Hands-on experience with vision encoders and contrastive models such as CLIP, SigLIP, or JEPA.
- Exposure to medical VLMs, imaging foundation models, or self-supervised methods such as DINOv2 or MAE.
- Experience building MCP servers in Python or TypeScript.
- Familiarity with medical imaging formats such as DICOM, HL7, and FHIR, and HIPAA/PHI handling.
- Awareness of MLOps tooling such as MLflow, Weights & Biases, and model registries.
- Awareness of FDA considerations for clinical AI, including 510(k) and PCCP.
Responsibilities
- Fine-tune and adapt open VLMs and imaging foundation models for report drafting, impression generation, finding detection, and prior comparison, using parameter-efficient methods such as LoRA and QLoRA as well as full fine-tuning under senior guidance.
- Work with vision encoders, contrastive image-text models, and self-supervised approaches; build and maintain embedding pipelines for retrieval, cohort discovery, case similarity search, and drift detection.
- Curate and version datasets from internal PACS and reporting systems, including DICOM header, burned-in pixel, and paired-report de-identification.
- Use Claude Code and OpenAI Codex daily to build, test, and maintain code; maintain project context files such as CLAUDE.md and AGENTS.md; help build and run MCP servers for internal system access.
- Deploy models through containerized inference and GPU serving, support CI/CD pipelines, and monitor production models for drift and performance.
- Build automated evaluation harnesses for clinical accuracy, negation, laterality, and hallucination scoring against radiologist ground truth.
- Build agentic and LLM-driven tools for operational workflows, such as converting unstructured requests into RIS scheduling actions and supporting intelligent operations dashboards.
- Develop initial clinical LLM pipelines for prior report summarization, scanned document summarization, and relevant prior selection.
Benefits
- Competitive compensation program to attract, retain, and motivate a high-performance workforce.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
