Backend Engineer

About

FutureHouse, a philanthropically funded AI research lab in San Francisco, laid the groundwork for AI agents capable of autonomously performing scientific research to accelerate basic discovery in biology. Edison Scientific, a spinout of FutureHouse, now carries this mission forward to build an AI Scientist - scaling the research, productizing it, and applying it to critical challenges such as drug development.

We are pioneering a new category: bringing AI Scientists to an industry accustomed to buying drug assets, not artificial intelligence. To succeed, we need entrepreneurial leaders who can combine strategic thinking, customer empathy, technical fluency, and relentless execution to bring this vision to market.

Role

As a Backend Engineer, you’ll support our core platform that automates scientific discovery. You will design and build data pipelines, APIs, and infrastructure. Our mission is to build an AI scientist, and you’re helping build the tools and data it will use.

By enabling scalable, reliable, and secure backend systems, you’ll be building the foundation that fuels breakthrough science. At FutureHouse, backend engineering isn’t just about servers and databases- it’s about accelerating humanity’s ability to ask bigger questions and find answers faster.

Responsibilities

• Design, implement, and maintain APIs, services, and databases scientific discovery platform.


• Implement monitoring, observability, and automated testing to maintain reliability and uptime.


• Troubleshoot and squash bugs.


• Collaborate with front-end, design, and research teams to build new AI agents and services.


• Be a member of a professional software engineer team working at the frontier of AI for science.

Qualifications

• 3+ years of experience in backend software engineering


• High proficiency in one or more backend languages (e.g., Python, Node.js, etc.)


• Strong experience designing and building APIs (REST, GraphQL)


• Proficiency in databases (PostgreSQL, MySQL, MongoDB, or similar), with an understanding of schema design and query optimization


• Hands-on experience with cloud infrastructure (AWS, GCP, or Azure) and containerization/orchestration tools (Docker, Kubernetes)


• Hands-on experience with CI/CD pipelines, automated testing, and monitoring/observability tools (e.g., Prometheus, Grafana, Datadog)


• Based in the San Francisco Bay Area or willing to relocate.

Bonus points for:

• Experience with scientific computing, large-scale data pipelines, or ML/AI infrastructure

Location: San Francisco, CA (In-Person)
Base Salary Range: $140,000- $175,000 (depending on experience)