{"schemaVersion":"jobsearcher.job.v1","id":"b109f98ad4aebdef3d53d455","url":"https://jobsearcher.com/jobs/b109f98ad4aebdef3d53d455","canonicalUrl":"https://jobsearcher.com/jobs/b109f98ad4aebdef3d53d455","title":"Founding Machine Learning Engineer","description":"Founding Machine Learning EngineerMedical Imaging AI Evaluation, Reliability & Evidence InfrastructureAbout the OpportunityMy client is building the infrastructure layer for evaluating and validating safety-critical AI systems. As AI becomes increasingly embedded in clinical workflows, benchmark performance alone is no longer enough. Healthcare providers, regulators, insurers, and patients need evidence that AI systems behave reliably across real-world environments, populations, scanners, and workflows.This company is working with leading medical imaging AI organizations and healthcare institutions to redefine how AI validation is performed, moving beyond static testing towards continuous evidence generation and monitoring.Their goal is to build the systems, methodologies, and tooling that allow organizations to understand how models behave in practice, identify risk, and generate defensible evidence for deployment and regulatory decisions.The role:This is not a traditional machine learning engineering role.You will not spend your time simply training models or chasing benchmark improvements.Instead, you will investigate how AI systems behave in real-world environments, determine where validation approaches break down, identify sources of risk, and help define what evidence is required to support safe deployment.The work sits at the intersection of:Medical Imaging AIMachine LearningEvaluation ModelRobustness & ReliabilityAI Safety & ValidationRegulatory EvidenceGeneration Software EngineeringAs one of the earliest technical hires, you will play a key role in shaping both the product and the methodology used to evaluate safety-critical AI systems.Investigate Model Behaviour & PerformanceDesign and execute evaluations for medical imagingAI systemsAnalyze performance across populations, institutions, scanners, imaging protocols, and clinical workflowsInvestigate failure modes, robustness limitations, and generalization gapsEvaluate distribution shift, demographic bias, subgroup performance, and deployment risksProduce evidence that supports, challenges, or refines claims about model performance and safetyDevelop AI Validation MethodologyDefine frameworks for structuring claims, arguments and evidenceDetermine what evidence is sufficient for deployment, regulatory, and clinicaldecision-makingChallenge assumptions and identify weaknesses in existing validation approachesTransform recurring investigations into repeatable workflows and reusable methodologiesHelp establish best practices for evaluating safety-critical AI systemsBuild Product & Evaluation InfrastructureWrite production-quality Python code supporting evaluation workflowsDevelop reusable investigation pipelines and benchmarking frameworksBuild agentic workflows that automate evidence generation and analysisPrototype customer-facing functionality using modern AI development toolsCollaborate with customers, researchers, clinicians, and regulatory stakeholders","company":"Established Search","rawCompany":"established search","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-02T00:00:46.279Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Founding Machine Learning Engineer","description":"Founding Machine Learning EngineerMedical Imaging AI Evaluation, Reliability & Evidence InfrastructureAbout the OpportunityMy client is building the infrastructure layer for evaluating and validating safety-critical AI systems. As AI becomes increasingly embedded in clinical workflows, benchmark performance alone is no longer enough. Healthcare providers, regulators, insurers, and patients need evidence that AI systems behave reliably across real-world environments, populations, scanners, and workflows.This company is working with leading medical imaging AI organizations and healthcare institutions to redefine how AI validation is performed, moving beyond static testing towards continuous evidence generation and monitoring.Their goal is to build the systems, methodologies, and tooling that allow organizations to understand how models behave in practice, identify risk, and generate defensible evidence for deployment and regulatory decisions.The role:This is not a traditional machine learning engineering role.You will not spend your time simply training models or chasing benchmark improvements.Instead, you will investigate how AI systems behave in real-world environments, determine where validation approaches break down, identify sources of risk, and help define what evidence is required to support safe deployment.The work sits at the intersection of:Medical Imaging AIMachine LearningEvaluation ModelRobustness & ReliabilityAI Safety & ValidationRegulatory EvidenceGeneration Software EngineeringAs one of the earliest technical hires, you will play a key role in shaping both the product and the methodology used to evaluate safety-critical AI systems.Investigate Model Behaviour & PerformanceDesign and execute evaluations for medical imagingAI systemsAnalyze performance across populations, institutions, scanners, imaging protocols, and clinical workflowsInvestigate failure modes, robustness limitations, and generalization gapsEvaluate distribution shift, demographic bias, subgroup performance, and deployment risksProduce evidence that supports, challenges, or refines claims about model performance and safetyDevelop AI Validation MethodologyDefine frameworks for structuring claims, arguments and evidenceDetermine what evidence is sufficient for deployment, regulatory, and clinicaldecision-makingChallenge assumptions and identify weaknesses in existing validation approachesTransform recurring investigations into repeatable workflows and reusable methodologiesHelp establish best practices for evaluating safety-critical AI systemsBuild Product & Evaluation InfrastructureWrite production-quality Python code supporting evaluation workflowsDevelop reusable investigation pipelines and benchmarking frameworksBuild agentic workflows that automate evidence generation and analysisPrototype customer-facing functionality using modern AI development toolsCollaborate with customers, researchers, clinicians, and regulatory stakeholders","datePosted":"2026-07-02T00:00:46.279Z","dateModified":"2026-07-02T00:00:46.279Z","hiringOrganization":{"@type":"Organization","name":"Established Search","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b109f98ad4aebdef3d53d455"},"url":"https://jobsearcher.com/jobs/b109f98ad4aebdef3d53d455"}}