Staff Software Engineer (Fullstack)

Alaffia Health•New York, NY

About The Position

Alaffia is a high-growth, venture-backed Series B healthtech startup based in New York City. It partners with health plans to reduce improper payments and administrative waste across the claims lifecycle through expert clinical insight and transparent AI. The Staff Software Engineer will address complex technical challenges across teams, set architectural direction across the stack, and increase the effectiveness of the engineering organization. The role translates strategic goals into technical roadmaps, leads execution, and promotes AI-augmented development practices. It will help scale GenAI applications and build React-based interfaces used by Payment Integrity and Operations teams, supported by backend architecture, system design, and cloud infrastructure. The position works with React and TypeScript on the frontend and Python or TypeScript on the backend, depending on team needs.

Requirements

10+ years of software engineering experience, including a record of leading complex, high-impact projects at scale and production-level API development and backend data processing.
Deep hands-on expertise in React and strong command of modern JavaScript and TypeScript, component architecture, and state management patterns.
Hands-on experience with relational databases and data model design.
Strong system design and cloud infrastructure experience; AWS is preferred.
Experience with CI/CD, containerization (Docker and Kubernetes), and observability practices such as logging, user activity metrics, and Datadog or similar tooling.
Experience building and scaling systems within a growing engineering team.
Experience collaborating cross-functionally with backend, product, and design teams to ship high-quality features, with a strong eye for UX.
Ability to work through ambiguity and translate operational or business requirements into technical solutions.
Experience with testing frameworks and CI/CD tooling for frontend and backend applications.
Demonstrated success adopting AI coding assistants to improve team productivity.
Excellent communication skills and experience mentoring senior engineers.

Nice To Haves

Experience building B2B SaaS products, particularly for operations, clinical, or financial teams, or experience in healthcare technology or another highly regulated industry.
Experience shipping user-facing AI products or deploying AI/ML models, including chat interfaces, agentic workflows, or natural-language interactions.
Familiarity with design systems and reusable, scalable component libraries.
Experience optimizing performance for data-dense, workflow-heavy applications.
Experience building SDKs or libraries.
Experience with vector databases.
Experience with data-processing orchestrators such as Airflow or Dagster.
Experience with message queue systems such as AWS SQS, Kinesis, or Kafka.
GraphQL and PostgreSQL experience.
Open-source contributions or technical publications.

Responsibilities

Architect and own delivery of complex, high-impact frontend and backend systems spanning multiple teams.
Set organizational technical direction and coding and design standards, including frontend architecture, state management, component design, and performance.
Design, build, and maintain production-grade React applications for core auditing and workflow tools.
Design, develop, and deploy data-intensive applications in production.
Partner with backend, product, and design teams to define API contracts and ensure end-to-end integration.
Improve frontend and backend infrastructure, including build tooling, testing practices, and CI/CD pipelines.
Drive adoption of AI coding tools such as Claude Code, establish best practices, and measure productivity gains.
Mentor senior engineers and influence technical decisions without formal authority.
Collaborate with data, DevOps, AI, product, and business teams to build a GenAI-powered claim review platform.
Improve UI performance, accessibility, and user experience across the platform.
Own testing strategy, system health, observability, and build-versus-buy decisions for critical infrastructure.
Debug and resolve complex issues across components and the stack.