Lead the design and implementation of end-to-end features across our web applications and backend services.
Build internal evaluation infrastructure for AI and LLM healthcare finance applications.
Architect and maintain scalable, secure, and reliable systems that integrate with various healthcare platforms.
Drive best practices in software development, including code reviews, testing, and documentation.
Bachelor's or Master’s degree in Computer Science, Software Engineering, or a related technical field.
Proficiency in TypeScript, React, Node.js, and Python.
Comfortable with LLM applications and containerized deployments.
Thrives in environments of complexity and ambiguity, prioritizing collaboration and seeking the best ideas regardless of their source.
A willingness to pitch in wherever needed—we move fast, and we need team players who are eager to contribute.
Hands-on work with LLM prompt engineering, finetuning, and RAG applications.
Experience with workflow automations.
Prior work in healthcare, finance AI, or other regulated, high-stakes industries.
Open-source contributions to ML libraries, evaluation suites, or benchmarking tools.
Experience with cloud platforms (e.g., Azure, GCP, AWS) and containerization technologies (e.g., Docker, Kubernetes).
Strong understanding of database systems, both relational and non-relational.
Familiarity with healthcare standards such as FHIR, HL7, and medical coding.
Similar Jobs
What you need to know about the San Francisco Tech Scene
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine



