Founding Engineer, AI Agents Platform

Compensation: $250,000–$275,000 (includes $200,000–$250,000 base + bonus) + equity

Location: San Francisco, CA (5 days in office)

Work Policy: On-site

Sponsorship: Open to visa transfers with at least 2 years remaining on H1B

Stage: Seed | $10M raised | ~2 engineers

Who Are We?

A stealth, seed-stage AI healthcare infrastructure company is building the core platform for computer-use agents in the most demanding enterprise environments. The company’s systems enable AI-powered agents to operate across real healthcare workflows, interact with enterprise software, move data through complex systems, and automate high-value processes with the reliability required for regulated industries.

This is not a narrow backend role and not another wrapper around frontier models. The team is building the infrastructure layer that turns emerging AI capabilities into dependable production systems: backend services, APIs, integrations, orchestration, state management, browser automation, observability, recovery, and reliability tooling for agents executing real enterprise workflows. The work sits at the boundary between classical distributed systems and next-generation AI products.

Founded in 2025 by an experienced, second-time founding team with prior HealthTech exits, the company has already raised $10M in seed funding and has strong early commercial traction, including hundreds of thousands in ARR and a solid procurement pipeline. You’ll join a tiny founding engineering team, report directly to the CTO, and help build the platform foundation for an AI company pursuing national-scale impact in healthcare operations.

What’s in It for You?

1. Founding-engineer scope: Join a ~2-engineer team and own core platform decisions while the architecture, culture, and product surface area are still being defined.
2. Top-of-market cash compensation: Earn up to $275K cash plus meaningful equity at the seed stage.
3. Greenfield infrastructure: Build from scratch across APIs, integrations, orchestration, state, recovery, observability, and production hardening.
4. AI-adjacent systems depth: Work on the infrastructure that makes computer-use agents reliable, observable, and deployable in real enterprise settings.
5. High-impact customer exposure: Own engineering relationships and feedback loops with customers using the platform for complex healthcare workflows.
6. Elite technical environment: Work alongside experienced builders from billion-dollar technology companies and top academic institutions.
7. Career-defining ownership: Grow with a well-capitalized, fast-moving startup where early engineers can become long-term technical leaders.

What Will You Do?

Backend Platform & APIs

1. Build core backend services that power customer-facing products, internal tooling, and AI-driven workflow automation.
2. Design and implement APIs, data models, schemas, integrations, and infrastructure for enterprise healthcare workflows.
3. Develop reliable data flows between healthcare systems, internal services, browser automation layers, and AI-powered agent infrastructure.
4. Write clean, correct, production-quality code in Python or similar backend languages such as Java, Scala, Rust, Go, or C++.

AI Agents Infrastructure

1. Build infrastructure for computer-use workflows, including orchestration, state management, retries, recovery, and execution monitoring.
2. Help turn experimental AI capabilities into dependable product infrastructure that can operate in messy real-world enterprise environments.
3. Develop product features that combine deterministic software, browser automation, model-driven execution, and backend systems.
4. Work close to applied AI systems without relying on “vibe coding” or black-box outputs; you’ll be expected to understand, reason, and build from first principles.

Reliability, Observability & Production Hardening

1. Own reliability, latency, debugging, and production hardening for systems used in high-stakes healthcare workflows.
2. Build observability, alerting, and debugging tools that make agent and workflow behavior understandable in production.
3. Diagnose complex failures across APIs, integrations, automation layers, customer environments, and model-driven execution.
4. Design resilient distributed systems that handle real-world variability, partial failure, rate limits, state drift, and operational edge cases.

Product Ownership & Customer Feedback

1. Own features end to end, from ambiguous customer problem through design, implementation, testing, deployment, and iteration.
2. Work directly with customers and founders to understand workflow pain points and translate them into reliable infrastructure.
3. Ship quickly in a technically ambitious environment while maintaining strong engineering quality.

What Will You Need?

1. 1–4 years of professional experience in backend engineering, infrastructure engineering, systems engineering, or backend-leaning full-stack development.
2. Strong CS fundamentals, including data structures, algorithms, memory, runtime and space complexity, systems design, and distributed systems tradeoffs.
3. Strong backend fluency, ideally with Python, and comfort with backend languages such as Java, Scala, Rust, Go, or C++.
4. Experience building APIs, data management systems, integrations, schemas, backend services, or production infrastructure.
5. Experience with resilient distributed systems, production reliability, debugging, observability, and operational hardening.
6. Ability to write clean, correct, maintainable, production-quality code.
7. Backend-leaning full-stack experience is welcome, but the role requires at least 50% backend depth.
8. Excitement about AI systems and the infrastructure required to make agents, automation, and computer-use products dependable.
9. Strong product sense and comfort working close to customers, founders, and ambiguous business problems.
10. High ownership, fast execution, strong communication, and a willingness to invent 0→1 infrastructure from scratch.
11. A first-principles engineering mindset: AI-forward, but able to reason independently and explain technical decisions clearly.
12. Bonus: browser automation, workflow systems, applied AI products, healthcare software, math competition background, entrepreneurial projects, or prior end-to-end ownership of shipped features.