Note: The job is a remote job and is open to candidates in USA. Established Search is focused on building evaluation and evidence infrastructure for safety-critical AI systems, particularly in diagnostic medical imaging. The role involves investigating how AI systems behave in practice, designing evaluations, and generating evidence necessary for deployment and regulatory decisions.

Responsibilities

• Design and execute evaluations for medical imaging AI systems
• Investigate model failure modes, robustness, and generalization gaps
• Analyze behavior across populations, scanners, imaging protocols, and clinical settings
• Determine what evidence is sufficient for stakeholders making deployment or regulatory decisions
• Translate technical findings into actionable recommendations for customers and clinical stakeholders
• Build reusable evaluation pipelines, evidence schemas, and model assessment frameworks
• Work with messy, incomplete, and noisy real-world clinical data
• Help shape how evaluation investigations are conducted across the organization

Skills

• Strong experience in machine learning for medical imaging (radiology, pathology, cardiology imaging, or related domains)
• Experience evaluating or validating real-world ML systems, not just training models
• Deep understanding of: model robustness, distribution shift, uncertainty, failure analysis, and real-world deployment behavior
• Strong Python skills across the full investigation workflow: data analysis, experimentation, evaluation, and reporting
• Experience working with noisy or imperfect clinical datasets
• Ability to communicate technical findings clearly to both technical and non-technical stakeholders
• High tolerance for ambiguity and open-ended investigative work
• Experience with FDA-regulated AI/ML systems or medical device submissions (510(k), De Novo, SaMD, etc.)
• Experience with medical imaging deployment evaluation or clinical validation
• Experience with interpretability, post-deployment monitoring, uncertainty estimation, or model auditing
• Experience designing reproducible evaluation frameworks or benchmarking systems
• Background in healthcare AI or other safety-critical ML domains
• Customer-facing or cross-functional technical leadership experience
• PhD or equivalent research depth in ML, medical imaging, computer vision, or related areas

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