Agent Architect
Hippocratic AIAbout the role
About Us
Hippocratic AI is developing the first safety-focused Large Language Model (LLM) for healthcare. Our mission is to dramatically improve healthcare accessibility and outcomes by bringing deep healthcare expertise to every person. No other technology has the potential for this level of global impact on health.
Why Join Our Team
Innovative mission: We are creating a safe, healthcare-focused LLM that can transform health outcomes on a global scale.
Visionary leadership: Hippocratic AI was co-founded by CEO Munjal Shah alongside physicians, hospital administrators, healthcare professionals, and AI researchers from top institutions including El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft and NVIDIA.
Strategic investors: We have raised a total of $278 million in funding, backed by top investors such as Andreessen Horowitz, General Catalyst, Kleiner Perkins, NVIDIA’s NVentures, Premji Invest, SV Angel, and six health systems.
Team and expertise: We are working with top experts in healthcare and artificial intelligence to ensure the safety and efficacy of our technology.
For more information, visit www.HippocraticAI.com.
We value in-person teamwork and believe the best ideas happen together. Our team is expected to be in the office five days a week in Palo Alto, CA unless explicitly noted otherwise in the job description.
About the Role
We are looking for an Agent Architect to design, develop, and innovate the next generation of agentic systems that drive our healthcare-focused AI platform. This individual will serve as a central architect in shaping how our agents think, act, and interact across diverse clinical use cases.
You will be responsible for selecting the right agentic paradigms—ranging from tool use, retrieval-augmented generation (RAG), and prompt engineering, to model training—and defining the underlying architecture for intelligent, safe, and responsive agents. You will work closely with our research, engineering, and evaluation teams to integrate cutting-edge techniques and continuously push the boundaries of agent capabilities.
This role blends deep technical knowledge with strategic thinking and experimentation. It’s ideal for those who thrive at the intersection of LLM system design, product innovation, and applied AI research.
Responsibilities
Architect and design new AI agents across a variety of clinical and operational use cases
Evaluate and select the optimal agentic paradigm for each scenario (e.g., tools, engines, prompting, RAG, model training)
Choose the appropriate models from our internal model library for specific tasks
Collaborate with the research team to fine-tune models and optimize agent behavior
Define and iterate on evaluation protocols in partnership with the evaluation team
Develop new agent patterns and workflows to enable novel capabilities and interactions
Rapidly incorporate state-of-the-art techniques from the latest scientific literature and open-source developments
Track and integrate capabilities from emerging foundational models to improve system performance and scope
Required Qualifications
5+ years in a technical field such as software engineering, machine learning, data science, or AI product development
Deep understanding of agentic system design and language model behaviors
Proficiency with Python and modern ML tooling
Experience building and evaluating non-deterministic AI/ML systems
Strong analytical and problem-solving skills, with an experimental mindset
Familiarity with LLM paradigms such as prompting, RAG, fine-tuning, and tool use
Preferred Skills
Experience designing agentic workflows or orchestration frameworks for LLMs
Background in AI research or exposure to cutting-edge developments in NLP
Ability to translate complex technical ideas into scalable architectures
Interest in healthcare applications, patient interaction design, and safety-critical systems
Excellent written and verbal communication skills, with the ability to clearly document design decisions and evaluations
Candidate Background
We recognize that agent architecture is a novel and rapidly evolving field. Ideal
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