Head of Quality Engineering

Rad AI•San Francisco, CA

15h•$215,000 - $250,000

About The Position

Rad AI is seeking a Head of Quality Engineering to lead its Quality organization and establish a consistent, visible quality system across embedded quality engineers working within product teams. Reporting to the CPO, this leader will build on an existing three-phase roadmap, quality baseline, and weekly incident review, spreading effective practices while standardizing where needed. Engineering teams will own the quality of what they build; the Quality organization will provide the strategy, systems, tools, coaching, and visibility that support that ownership. The role will integrate risk-based testing into planning and release decisions, bring representative radiologist workflows and customer environments into testing, and improve the reliability and usefulness of test signals. It will define consistent quality metrics and OKRs, publish a company-level quality dashboard, and lead quarterly quality reviews. The Head will also connect incident and customer-defect learnings to prevention, establish evaluation practices for non-deterministic AI outputs, and ensure clinical risk, HIPAA, auditability, and regulatory expectations are reflected in quality practices. The leader will hire, coach, and develop quality engineers; own the Test Automation Engineer career framework; and manage quality tools, vendors, and test infrastructure cost-effectively. The role will also document standards and build a company-wide Quality community of practice through activities such as Quality Week, office hours, and demos. The posting describes San Francisco onsite and United States remote arrangements; it does not specify which arrangement applies to this specific role.

Requirements

10+ years of experience in software quality and test engineering, including 5+ years leading quality teams.
A track record of building a quality function and staffing it cost-effectively as a company grows.
Experience taking over a capable but inconsistent quality organization and raising its standards by spreading effective practices rather than rebuilding the organization.
Strong hands-on test automation foundation, including end-to-end, API, and desktop or Electron UI automation in CI/CD pipelines, as well as test environments.
Ability to go deep on test architecture and flaky-test root causes.
Experience shifting quality left so engineers own quality and the quality team builds the system without becoming a release gate.
Data-driven experience defining quality metrics such as escaped defects, change failure rate, and test stability, setting OKRs, and improving the metrics.
Experience with risk-based testing for integration-heavy products across varied customer configurations and environments.
Ability to build a long-term quality roadmap and work with engineering leaders on architecture and testability decisions.
Excellent communication with technical and non-technical audiences and a record of influencing Engineering, Product, Support, and executives without formal authority.
Expertise hiring and managing distributed teams across time zones, including hiring, onboarding, career frameworks, and performance management.
Experience managing quality tooling and vendor relationships, such as test management platforms, outsourced QA, or test-environment providers.
A habit of asking strong questions, starting with the smallest valuable change, and using AI tools to make quality work faster and broader.

Nice To Haves

Experience in healthtech or a regulated environment, including HIPAA, PHI, or SOC 2; familiarity with medical-device software practices such as SaMD, IEC 62304, or ISO 14971 risk management.
Familiarity with radiology workflows and systems, including PACS, RIS, EHR, dictation platforms such as PowerScribe or Fluency, HL7/FHIR, FHIRcast, or DICOM.
Experience evaluating production ML or AI systems with non-deterministic outputs, such as speech-recognition accuracy or generated-text quality.
Ownership of performance, load, or reliability testing and close partnership with incident management.
Experience leading a multi-team quality organization, including managers or Staff-level engineers.
Experience building or scaling a quality function at a high-growth startup.

Responsibilities

Lead the Quality organization and establish a shared operating cadence, priorities, standards, and roadmap for embedded quality engineers.
Own the quality system by providing strategy, risk assessment, tooling, test environments, coaching, and visibility while enabling developers to own product quality.
Standardize test case management, suite organization, and CI integration across product areas.
Integrate test strategy and risk assessment into planning with Product, Engineering, Design, and Clinical; optimize coverage across unit, API, and end-to-end tests.
Establish a risk-based release lifecycle, including pre-release risk assessment, radiologist-persona test cases, minimum smoke-test expectations, and explicit hotfix testing expectations.
Develop an organization-wide definition of release readiness that accounts for customer quality-control processes and supports flexible change management.
Define radiologist personas and representative workflows with Product and Clinical, and expose the team to real workflows through shadowing, customer recordings, and product telemetry.
Bring customer-matched environments—including Windows workstations, dictation systems, PACS/RIS integrations, and site-specific configurations—into testing; determine where exploratory or manual testing adds value.
Own test stability, including failure triage, policy for quarantining and fixing flaky tests, stable environments with platform and infrastructure teams, and rationalization of duplicated or low-value coverage.
Define and report quality metrics, including customer-found versus internally found defects, escaped defects by product area, change failure rate, test stability, and time to detect.
Set quality OKRs, publish a company-level quality dashboard, and run quarterly quality reviews with Engineering and Product leadership.
Partner with incident review owners, Support, and Customer Success to turn corrective actions and customer-defect learnings into tests, observability, fixes, regression coverage, and updated risk profiles.
Maintain a quality bar for root cause analyses and ensure Quality learns from customers without becoming a Tier 2 support desk.
Establish evaluation practices for non-deterministic AI outputs, including speech recognition and generated impressions, in partnership with ML and product teams.
Partner with Clinical, Security, and Compliance to reflect patient impact, HIPAA, auditability, and regulatory expectations in quality practices.
Document quality standards, build a Quality community of practice, run Quality Week, office hours, and demos, and share feedback with engineers.
Hire, coach, and develop quality engineers; own the Test Automation Engineer career framework; manage quality tooling, vendors, and test infrastructure costs; and use AI to improve quality-work efficiency and reach.