Fullstack Engineer

Recruiting From Scratch•San Francisco, NY

5d•Hybrid

About The Position

The company is building an AI-powered operating system for healthcare providers that brings billing, payroll, scheduling, clinical documentation, parent communication, and other business operations into one integrated platform. It combines modern software with autonomous AI agents to reduce administrative complexity and help clinicians focus on patient care. The company has grown rapidly, reached $2 million in revenue within eight months, and recently raised a $10 million Series A. The engineering team is expanding to support the growing customer base and build the platform foundations for the next stage of growth. The Full-Stack Software Engineer will join a small engineering team and have meaningful ownership of core systems, architecture, and product development. The work spans backend systems, AI infrastructure, financial workflows, and user-facing products. The role is backend-heavy but includes frontend work as needed, with responsibility for delivering features end to end and maintaining reliability, data integrity, and security in production.

Requirements

Professional software engineering experience building and maintaining production systems.
Strong proficiency in Python; Python is a non-negotiable requirement.
Strong backend engineering experience, including the ability to design and own complex systems.
Experience with databases, data models, APIs, and application architecture.
Ability to take ownership of projects and drive them independently with minimal coaching.
Experience building systems where accuracy, reliability, and data integrity are important.
Strong problem-solving skills and ability to handle complex business logic and operational edge cases.
Experience working in fast-paced environments with a high engineering bar.
Strong attention to detail and a consistent track record of delivering high-quality work.
Comfort working across backend and frontend systems as needed.
Ability to work from the New York or San Francisco office at least three days per week.
Must already be authorized to work in the United States without visa sponsorship.

Nice To Haves

Experience at high-performing fintech or healthcare technology companies.
Experience building systems involving financial transactions, payments, billing, or payroll.
Experience in healthcare, insurance, logistics, or other operationally complex industries.
Experience with NoSQL databases, particularly MongoDB or Firebase.
Experience building AI-powered products or integrating LLMs into production workflows.
Experience designing state machines, domain models, and systems with complex business rules.
Experience working with data-sensitive or high-accuracy systems.
Experience working in small, high-performing engineering teams.
Experience taking ownership of projects with limited supervision.
Experience building products from early stages and scaling them as the business grows.

Responsibilities

Design and build foundational platform systems, including data models, APIs, state machines, business logic, and production infrastructure.
Develop AI-powered clinical products for documentation, treatment planning, reassessments, supervision, and administrative work.
Build revenue and payment infrastructure for service authorizations, billing, payment adjudication, and provider payouts.
Develop provider operations tools for credentialing, contracting, compliance, payroll, scheduling, and staffing.
Build patient and family products for intake, onboarding, scheduling, communication, progress tracking, and payments.
Develop software and AI systems that automate internal workflows across billing, authorizations, credentialing, and care coordination.
Own features end to end across backend services, database schemas, APIs, and frontend experiences.
Make architectural decisions, establish reliable data models, enforce business rules, design state machines, and build scalable systems.
Contribute to backend-heavy projects while working on frontend functionality and product experiences as needed.
Use AI tools in daily engineering work and identify opportunities to improve development and operational processes with AI.
Maintain production reliability, data integrity, security, and accuracy.