Clera - remote - Global - Construction & Infrastructure
This is a founding-team opportunity at a small, fast-moving healthtech AI company building evidence infrastructure for safety-critical medical imaging AI. As a Founding Forward-Deployed ML Engineer, you will sit at the intersection of research, product deployment, and clinical operations — working directly with hospital partners to evaluate and deploy medical imaging AI in real-world clinical settings.
You will play a central role in bridging the gap between benchmark performance and clinical reliability, translating AI models into trusted tools for patient care. This is a high-ownership, high-impact role suited to someone who is equally comfortable writing code, navigating clinical environments, and driving cross-functional projects to completion.
Work arrangement: Hybrid, on-site in Sunnyvale, CA. Travel to hospital partner sites required as needed.
Visa sponsorship: Not available.
Build reproducible evaluation pipelines and validation workflows for medical imaging AI in clinical settings.
Lead forward-deployed engagements by working on-site with hospital partners to integrate models into clinical workflows.
Analyze model generalization, failure modes, and uncertainty to inform clinical reliability assessments.
Integrate ML models into clinical imaging systems and radiology pipelines (DICOM/PACS).
Translate clinical needs into technical requirements and drive cross-functional projects through to completion.
Support regulatory submissions and clinical evaluations (FDA pathways such as 510(k) and De Novo) and maintain related documentation.
Ensure data privacy and regulatory compliance (HIPAA) across all ML deployments.
Establish and maintain MLOps practices for deployment, monitoring, and ongoing evaluation.
Required (dealbreakers):
2+ years of Machine Learning / Engineering experience.
Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field — or equivalent practical experience.
Required skills & experience:
Hands-on expertise in medical imaging workflows and integration: DICOM/PACS, radiology pipelines, and integrating ML models into clinical systems.
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