Clarity Pediatrics

Clarity Pediatrics

Growth Engineer

San Francisco Bay Area · Exec

Sponsorship not specified$175k-$210kDetected 61 days ago
TypeScriptReactNext.jsCSSNode.jsSpringGitPostgreSQLRedisAWSGCPGitHub ActionsUX ResearchPlaywrightHL7/FHIREHR/EMRResearchLeadershipCollaboration

About the role

  • This is a builder's role with full product authority.
  • You'll report to our Head of Product, with a dotted line to our Head of GTM.
  • Your primary focus is provider referrals.

Responsibilities

  • research, ideation, build, ship, and measure.
  • You'll own the surfaces that drive top-of-funnel acquisition without sales or marketing dependency.
  • You set the strategy, write the spec, ship the code, and own the metric.
  • You'll work closely with clinical, operations, sales, and engineering - but you'll largely operate independently on the surfaces you own.
  • Self-serve product experiences that make it effortless for pediatricians and their staff to navigate Clarity, refer patients, and get answers without contacting support.
  • We're hiring a Growth Engineer to own product-led growth at Clarity end-to-end: research, ideation, build, ship, and measure.
  • We're building a virtual pediatric specialty clinic that delivers trusted, science-backed care to all families.

Requirements

  • 6+ years shipping product end-to-end.
  • You can prototype in a weekend and harden for production the next week.

Nice to have

  • Aptible (AWS or GCP experience is a plus)
  • Provider workflow tools, EMR/EHR integrations (FHIR/HL7), e-fax/clinical messaging, prior auth, or referral and care-coordination products are especially relevant.
  • Infrastructure: Aptible (AWS or GCP experience is a plus)

Benefits

  • medical, dental, vision, and 401(k).
  • Our leadership team has deep healthcare expertise.
  • Our Chief Medical Officer trained at UCSF and Stanford and previously built specialty care services at Kaiser Permanente.

This listing is sourced directly from Clarity Pediatrics's careers page and normalized into a canonical job model.