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Blossom Health

Software Engineer

New York City$150k–$220kfulltimemidAdded today

About this role

Blossom Health, a Series A mental health tech startup, is seeking a Software Engineer to design and deploy scalable systems for an AI-native care platform serving clinicians and patients. You'll own full-stack features end-to-end in a high-trust environment where your code directly expands access to affordable mental healthcare.

What you'll do

  • Design and build scalable, reliable systems powering core product workflows
  • Own features and projects end-to-end from problem understanding through implementation and iteration
  • Ship high-quality code balancing speed with technical rigor
  • Leverage AI to create intuitive experiences for clinicians and patients
  • Collaborate with engineers, clinicians, and product teams to solve healthcare problems
  • Participate in code reviews and maintain high engineering standards

What they're looking for

  • TypeScript and/or Python
  • LLM-powered applications (preferred)
  • Full-stack system design
  • Production systems development
  • Reliability and observability practices
  • Healthcare domain knowledge
  • API and database design
  • Rapid technology adoption

Benefits

  • Competitive salary and equity
  • Best-in-class health, dental, and vision insurance
  • Unlimited PTO with outcomes-focused culture
  • Team offsites and outings
  • Well-stocked NYC office
  • Growth opportunities in a Series A hypergrowth environment
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Blossom Health

Blossom Health builds an AI-native mental healthcare platform designed to expand access to psychiatric care. The company is hiring software engineers and AI engineers to develop scalable systems and own meaningful projects across the full product lifecycle.

View all jobs at Blossom Health

Likely interview questions

  • Tell us about a production system you built end-to-end—what were the key design decisions and how did you handle scalability?
  • Describe your experience with LLM-powered applications and how you've approached reliability in AI-dependent features.