Sr. Full-Stack Engineer

Edison Scientific•San Francisco, CA

8d•$200,000 - $450,000•Onsite

About The Position

About Edison Scientific focuses on building and commercializing AI agents for science, and shares FutureHouse’s mission to build an AI Scientist - scaling autonomous research, productizing it, and applying it to critical challenges such as drug development. Role As a Senior Full-Stack Engineer (Backend-Focused), you’ll play a key role in shaping and scaling our core platform that automates scientific discovery. You’ll work across the stack, with a strong emphasis on backend systems- designing and evolving services, APIs, data pipelines, and infrastructure- while also contributing to critical user-facing workflows. Our mission is to build an AI scientist, and you’ll help define and build the tools, interfaces, and data systems it relies on. This role goes beyond implementation: you’ll influence architectural direction, establish engineering best practices, and partner closely with frontend engineers, designers, and researchers to deliver robust, end-to-end solutions. At Edison Scientific, full-stack engineering at the senior level is about technical ownership and leverage- understanding how complex systems interact, making sound architectural tradeoffs, and building foundations that allow teams and science to move faster.

Requirements

5+ years of professional software engineering experience, with significant experience across backend systems and full-stack development.
Deep proficiency in one or more backend languages (e.g., Python, Node.js), with a strong track record of designing production systems.
Extensive experience designing, building, and evolving APIs (FastAPI, REST; GraphQL a plus).
Strong working knowledge of modern frontend frameworks (e.g., React, Next.js) and how frontend and backend systems interact at scale.
Expertise with relational and/or document databases (PostgreSQL, MySQL, MongoDB, or similar), including schema design and performance considerations.
Hands-on experience with cloud infrastructure (AWS, GCP, or Azure) and containerized environments (Docker; Kubernetes preferred).
Proven experience building and maintaining CI/CD pipelines, automated testing strategies, and observability systems.
Ability to operate autonomously, make sound technical judgments, and drive projects from concept through production.
Strong communication skills and experience collaborating across disciplines in fast-moving environments.

Nice To Haves

Experience with data-intensive platforms, scientific computing, or ML/AI infrastructure.
Prior experience in startups or small teams with significant architectural ownership and ambiguity.
Experience scaling systems, teams, or platforms through periods of rapid growth.

Responsibilities

Architect, design, implement, and maintain backend services, APIs, and databases that power the scientific discovery platform.
Drive technical decisions across the stack, balancing scalability, reliability, security, and developer velocity.
Lead the design and evolution of data pipelines supporting AI agents and research workflows.
Contribute to and review frontend features and workflows, ensuring seamless integration with backend systems.
Establish and uphold best practices around testing, monitoring, observability, and incident response.
Troubleshoot complex, cross-system issues and guide others through effective debugging and remediation.
Collaborate closely with engineering, design, and research teams to scope, plan, and ship new AI-driven capabilities.
Mentor and support other engineers, raising the technical bar and fostering a strong engineering culture.
Act as a trusted technical partner in a highly collaborative team working at the frontier of AI for science.