About the Role
Join a small, mission-driven team conducting rigorous, independent evaluations of medical imaging AI systems — bridging the gap between benchmark performance and real-world clinical reliability. As a Medical AI Researcher, you'll work directly with medical imaging companies preparing FDA submissions, owning customer engagements end-to-end: from defining evaluation questions to delivering evidence that informs go/no-go decisions. You'll combine strong ML skills with customer-facing judgment to characterize model behavior, generalization, and uncertainty in real-world clinical workflows.
What You'll Do
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Lead end-to-end customer engagements — run meetings, define evaluation questions, and scope investigations.
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Design and execute investigations that characterize model behavior, generalization, failure modes, and remaining uncertainty.
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Analyze medical imaging workflows (DICOM/PACS, radiology pipelines) and translate findings into actionable evaluation evidence.
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Deliver clear, defensible reports and presentations for regulatory and internal audiences under tight timelines.
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Collaborate with customers and cross-functional teams to inform product strategy and go/no-go decisions.
What We're Looking For
Required:
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Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience).
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Hands-on expertise with medical imaging workflows and integration — DICOM/PACS, radiology pipelines, and integrating ML models into clinical systems.
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Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow).
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Practical MLOps and model evaluation skills: building reproducible evaluation pipelines, model validation/monitoring, Docker, and Kubernetes.
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Several years of experience in ML evaluation or medical imaging AI.
Nice to Have:
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Healthcare industry experience.
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Familiarity with regulatory considerations for medical AI, including FDA submissions such as 510(k) or De Novo.
Compensation & Benefits
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Salary: $150,000 – $230,000 USD annually
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Visa sponsorship is not available for this role.
Location
This is a full-time, on-site role based in San Francisco, CA. Remote work is not available.
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