• Design and optimize data pipelines (ETL/ELT); • Ensure data quality, integrity and availability; • Lead Data Engineering and MLOps strategies; • Support training, inference, and monitoring of ML models; • Mentor team members and support technical decision-making.

• Design and build various high-performance backend microservices • Integrate data from our AI/Ml classification algorithm • Collaborate with Product, Design, and Developers to create an amazing user experience • Architect and implement data models, API functionality, and RESTful interfaces • Help automate data-flows between different entities and organizations while maintaining HIPAA and SOC 2 compliance • Manage code in GitHub and maintain the CI/CD pipeline • Collaborate with other developers: AI/ML, iOS, Android, QA, and management to develop high-quality products • Engage with cross-functional teams including hardware and medical professionals to ensure a seamless user experience.

• Build and maintain backend services and APIs in Python, using AI-assisted development and automation as a standard part of the workflow • Own features end-to-end, from design through production, with accountability for quality, performance, and maintainability • Actively drive AI adoption within the engineering team by introducing, refining, and scaling AI-driven workflows across the development lifecycle • Identify opportunities where AI can reduce manual effort, improve code quality, accelerate delivery, or improve operational reliability • Develop and support single-page applications using Vue • Work closely with engineers in small, cross-functional feature teams • Collaborate with Product Managers and other teams to deliver measurable business value • Partner with your Engineering Manager to support technical growth, mentorship, and career development • Support other tasks or projects as assigned to meet team and business needs

• Ship features and customer-facing enhancements across the full stack, working from product specs and customer feedback. • Investigate and fix customer-reported bugs in your area of the product — performance regressions, edge cases, integration breakage — with the self-sufficiency to dive into unfamiliar code and deliver a fix. • Use AI-assisted development tooling (Claude Code, Cursor, etc.) as a first-class part of the dev loop — including writing tests and evals for AI-generated changes, and exercising judgment about when AI output is shippable. • Contribute to monolith decomposition and modernization as touch points emerge in your work. • Implement and maintain modern automated testing — unit, integration, and AI-component evals. • Collaborate with cross-functional teams to define, design, and ship; coordinate deployments and incident response with peers. • Advocate for incremental delivery and contribute to the engineering culture that surrounds you.