Staff Clinical Product Specialist

Accountabilities:

• • Provide clinical leadership and guidance to Product and Engineering teams on patient safety, Trust & Safety, error detection, and root cause analysis.
  • Monitor AI-generated clinical summaries and notes using human annotations, automated surveillance, and client feedback; identify, quantify, categorize, and communicate emerging error trends.
  • Develop and validate automated root cause analyses of clinical AI errors and conduct manual investigations when required.
  • Translate clinical findings and safety risks into clear product requirements, repair recommendations, prioritization decisions, and urgent escalations.
  • Lead clinical oversight of human-in-the-loop review operations, with a focus on reviewer accuracy, efficiency, quality, and performance.
  • Support reviewer operations through scheduling, hiring, onboarding, training, auditing, feedback, and allocation of special projects.
  • Partner with Data Science and Data Analytics teams to refine clinical error taxonomies, severity definitions, evaluation methodologies, and governance frameworks.
  • Collaborate on clinical prompt engineering, testing, and optimization of AI adversarial-detection systems, including development and curation of gold-standard evaluation datasets.
  • Supervise and guide Associate Clinical Specialists across data collection, analysis, prompt testing and validation, reviewer coordination, and communications.
  • Present clinical insights and recommendations to cross-functional stakeholders and contribute to Trust & Safety meetings and relevant internal, marketing, and sales initiatives.
  • Help strengthen the clinical evaluation framework for AI-enabled healthcare solutions by identifying emerging risks and opportunities for continuous improvement.

  Requirements:

  • MD or DO degree with at least 5 years of patient-care experience, including a minimum of 2 years of inpatient clinical experience.
  • Strong knowledge of clinical documentation, patient safety, healthcare quality, and clinical risk management.
  • Demonstrated experience analyzing quality or clinical data and communicating findings effectively to cross-functional stakeholders.
  • Ability to translate complex clinical risks and observations into actionable product requirements and prioritization recommendations.
  • Strong written, verbal, analytical, communication, and stakeholder-management skills.
  • Previous experience training, auditing, supervising, or managing clinical professionals or reviewers.
  • Familiarity with generative AI, machine-learning evaluation, healthcare technology, or AI-enabled clinical workflows.
  • Collaborative leadership style with the ability to motivate others, build trust, and influence teams across different disciplines.
  • Experience evaluating AI-generated clinical documentation is strongly preferred.
  • Experience with clinical prompt engineering is a plus.
  • Proficiency with SQL, Python, business intelligence platforms, or advanced spreadsheet analysis is advantageous.
  • Additional certification or graduate training in patient safety, healthcare quality, clinical informatics, responsible AI, or data analytics is a plus.
  • Must currently reside in the United States and be able to work remotely.
  • Candidates must be willing to complete a background check following an accepted offer.
  • Employment-based visa sponsorship for new employment-based visa status is not available for this role.

  Benefits:

  • Competitive compensation: $250,000–$260,000 base salary plus bonus.
  • Remote work: Fully remote position for candidates residing in the United States.
  • High-impact work: Opportunity to influence the safety, quality, and clinical reliability of AI-enabled healthcare products.
  • Cross-functional exposure: Direct collaboration with Product, Engineering, Data Science, Data Analytics, and clinical teams.
  • Leadership opportunity: Scope to supervise clinical specialists and help shape human-in-the-loop review operations.
  • Professional growth: Exposure to generative AI, clinical informatics, machine-learning evaluation, AI safety, and healthcare technology.
  • Mission-driven environment: Work focused on improving clinical quality and patient safety through responsible use of AI.