Hippocratic AI builds the safest generative AI healthcare agent for health systems, payors, and pharma. Over 180 million clinical interactions across 1,000+ use cases with 60+ partners worldwide. Backed by General Catalyst, Kleiner Perkins and a16z.
About the role
Design and build production-grade AI pipelines that power our voice-based generative healthcare agents—architecting RAG systems, multi-step reasoning workflows, and streaming interactions that scale reliably Collaborate cross-functionally with product, clinical, and engineering teams to translate healthcare workflows into safe, scalable, and human-centered AI experiences—bringing together research capabilities and product intuition
What they're looking for
- BS in Computer Science or equivalent
- 5+ years of professional experience in software, ML, or AI engineering
- Proven track record building and shipping AI- or ML-powered products in production environments
- Strong programming skills in Python with experience in distributed systems, APIs, and data pipelines
- Deep understanding of prompt engineering, vector databases, and retrieval systems (RAG), voice agents or willingness to learn rapidly
- Experience with cloud environments (AWS/GCP/Azure) and modern DevOps practices (Terraform, CI/CD, monitoring)
More about this role
As an AI Engineer at Hippocratic AI, you'll design and build the intelligent systems that power clinically safe healthcare agents at scale. You'll work at the intersection of large language models, real-time voice, and human-centered product design—translating cutting-edge research into production systems that patients and providers trust. This role exists because the gap between research and reliable production AI is where real innovation happens: your work directly determines whether advanced AI becomes a tool that transforms healthcare or remains a research project.
Own your first major outcome: By day 90, you will have shipped an end-to-end AI feature or system improvement (a new RAG pipeline, multi-agent workflow, or voice agent capability), validated it works reliably in production, and established the development patterns that the team will scale going forward.
Drive lasting impact: At 12 months, you will have built multiple production AI systems (RAG pipelines, multi-step reasoning workflows, real-time voice agent interactions), contributed architectural patterns that become standard across the team, improved our model evaluation and safety testing capabilities, and...
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