Hippocratic AI
Agent Deployment Engineer (Residency Program)
About this role
Hippocratic AI seeks an Agent Deployment Engineer to develop and optimize prompts for healthcare conversations using their safety-focused LLM platform. You'll work cross-functionally with engineers, product managers, and clinicians to build autonomous clinical dialogue systems, combining software engineering with advanced prompting techniques.
What you'll do
- Design and iterate complex prompts for the conversation planning layer to enable agentic workflows
- Collaborate with product, engineering, and clinical teams to create fully autonomous patient conversations
- Experiment with and analyze LLM outputs to refine prompt strategies and improve clinical safety
- Apply advanced prompting techniques to optimize model performance and patient experience
- Configure and test prompts to ensure engaging, clinically appropriate interactions
- Conduct outcome analysis to drive continuous improvement in conversation quality
What they're looking for
- Python programming
- LLM prompting (ChatGPT, Claude, etc.)
- Database design and querying
- RESTful API development
- Problem-solving and experimentation
- Backend development frameworks (Flask, Django, Spring Boot)
- AI/ML concepts
- Data privacy and security practices
Benefits
- Work on transformative healthcare AI technology
- Collaborate with experts from Stanford, Google, Meta, NVIDIA, and leading hospitals
- Join a well-funded company ($404M total, $3.5B valuation)
- Five-day in-office requirement in Palo Alto supporting team culture
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Hippocratic AI
Hippocratic AI builds safety-focused generative AI systems for healthcare, developing LLM-powered clinical dialogue platforms and integrations with electronic health records. The company is hiring infrastructure engineers, performance specialists, prompt engineers, and integration engineers to optimize AI inference, ensure system reliability, and connect healthcare data ecosystems.
View all jobs at Hippocratic AILikely interview questions
- Walk us through your experience with LLM prompting (ChatGPT, Claude, etc.). Can you describe a specific project where you optimized prompts and what metrics you used to measure success?
- Tell us about a time you collaborated with non-technical stakeholders (like product or domain experts). How did you translate their requirements into technical solutions?