Staff Software Engineer, Applied AI
About the Team We're an incubator-style team inside Parachute Health, scaling AI capabilities across the entire product ecosystem.
What you'll do Design and build agentic LLM pipelines that power AI features across the product.
Build full-stack product features: Python/FastAPI web applications, TypeScript or React web tooling.
Prompt-engineer and iterate on LLM prompts and models. Evaluate and recommend improvements based on accuracy, latency, and cost.
Strengthen our evaluation framework: curated datasets, evals, experiments, and regression identification, so we can ship changes with confidence.
Improve production observability: accuracy signals and feedback loops.
Contribute to architecture and technical direction.
Actively partner with other product teams to understand their AI use cases and identify how to best enable and support them.
Advise on when to apply an AI/LLM solution, and when not to: championing AI where it drives speed and ROI and steering away where it doesn't.
Requirements 5+ years of professional software engineering experience
Experience working in a startup, incubator, or other fast-paced 0-to-1 environment where you own features end-to-end and set your own direction.
Strong full-stack web application experience in Python.
Has shipped reliable agentic LLM workflows to production, with any orchestration framework (LangGraph, LangChain, etc).
Experience evaluating and testing LLM workflows: eval harnesses, golden datasets, regressions, and observability.
Familiarity with prompt engineering and the prompt iteration lifecycle.
Fluent with AI-powered coding tools (e.g. Cursor, Claude Code) to move quickly.
Strong written and verbal communication; able to drive alignment without being asked.
Must reside in the U.S. Nice to Have Healthcare workflows or health-tech experience.
Ruby on Rails experience
Parachute Health is hiring for this role on CareerPlace.
Apply on the company website through Parachute Health.
Imported from employer careers page. Source ref: 84d7a10171fa.
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