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# Remote

- **Company:** Parachute Health
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $200,000.0 - $260,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cursor (Graphical User Interface Elements), Python (Programming Language), Ruby on Rails, Software Engineering, Web Applications, Large Language Models, Prompt Engineering
- **Published:** August 25, 2026
- **Apply:** https://job-boards.greenhouse.io/parachutehealth/jobs/5220321007

## About the Role

* 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
 * A track record of evaluating emerging AI tools and driving their adoption on a team.

## Description

* 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.


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