Senior Software Engineer

Berry Street•New York, NY

7d•Onsite

About The Position

Berry Street is building AI-native healthcare infrastructure and consumer experiences, using insurance to make metabolic care more accessible. Its platform connects patients with more than 2,000 clinicians and AI tools to support care across chronic and acute conditions, and works with health plans, health systems, and care management companies in all 50 states. This founding-caliber engineering role works directly with the founder and leadership team to build systems and product experiences that support growth from hundreds of thousands of patients to millions. The engineer will help shape the technical direction of the platform, build reliable AI capabilities for clinical settings, and own work across provider- and patient-facing products from planning through production. The role involves close engagement with patients and providers and a focus on shipping quickly while maintaining quality.

Requirements

At least 5 years of engineering experience.
Experience building and maintaining production systems at scale and making consequential architectural decisions.
Experience shipping AI-driven systems and a strong perspective on the future of AI.
Product-oriented mindset, with the ability to understand user needs, question unclear specifications, and propose alternatives.
Full-stack capability with a back-end and platform/API emphasis.
Familiarity with React, Next.js, NestJS, TypeORM, PostgreSQL, AWS, TailwindCSS, and TypeScript.
Ability to take projects from an ambiguous idea to a shipped, working outcome with minimal supervision.
Ability to identify problems early, drive decisions, and follow through.
A bias for speed paired with sound judgment about when to move quickly and when to avoid cutting corners.

Responsibilities

Architect and ship AI-native systems, including autonomous clinical agents and LLM-powered workflows.
Build intelligent tools to improve dietitian effectiveness and support prescribing and monitoring GLP-1s at scale.
Develop infrastructure for reliable clinical AI, including evaluation pipelines, guardrails, and feedback loops.
Set technical direction and make architectural decisions for the platform as it scales.
Own features end-to-end across provider- and patient-facing products, from scoping and architecture through production.
Improve engineering code quality, tooling, and practices.
Work directly with patients and providers to understand their needs and translate them into systems that scale.