Applied AI Systems Engineer – San Francisco, CA (On-Site)
💰 Compensation: $140,000 – $200,000 + Equity (0.15% – 0.75%)
🛂 Visa Sponsorship: Available (STEM OPT, H1-B transfer, TN-1)
About the Role:
We’re partnering with a well-capitalized healthtech startup with 36+ months of runway, building advanced AI systems to automate patient access to life-saving treatments. With an incredibly small, elite team, joining now is a rare opportunity to take on meaningful ownership and receive significant equity grants.
As an Applied AI Systems Engineer, you’ll work hands-on with AI agents in production—improving behavior, designing evaluation frameworks, and building internal tooling to ensure reliability, scalability, and real-world impact. This is a highly technical, fast-paced, and mission-driven role.
Key Responsibilities:
- Improve real-world AI agent performance in production systems.
- Trace and resolve runtime bugs; implement regression testing for long-term stability.
- Design evaluation datasets and red-team simulations to stress-test workflows.
- Build internal QA tools, observability dashboards, and agent simulation frameworks.
- Develop data pipelines to normalize and transform unstructured client datasets.
- Set up automated testing, monitoring, and latency tracking infrastructure.
- Collaborate cross-functionally to scale systems impacting patient access to critical treatments.
Required Skills & Experience:
- Strong proficiency in Node.js, TypeScript, and Python.
- Hands-on experience with LLMs, prompt engineering, model evaluation, and agent orchestration.
- Skilled at debugging multi-step AI agent workflows.
- Experience building pipelines for unstructured or messy datasets.
- Solid understanding of scalability, reliability, and performance trade-offs.
- Familiarity with modern AI coding tools (e.g., Cursor, GitHub Copilot, Claude).
Preferred Background:
- 2–7 years working with AI/LLM-based systems in production.
- Direct experience designing evaluations, writing prompts, chaining model calls, or building agents.
- Prior work on QA tooling, agent simulation, or reliability frameworks.
- Track record in fast-paced AI product development cycles.
- High adaptability, ownership, and comfort working in ambiguous environments.
Why Join:
- Hands-On Impact: Work directly on production-grade AI agents, improving patient access.
- Elite Team: Collaborate with founders and engineers from Tesla, Google, YC, and MIT.
- Rapid Growth with Stability: Profitable with long-term enterprise contracts.
- Significant Equity Opportunity: Small team + investor support allows for meaningful equity grants.
