AI Engineer (Senior, Staff, Senior Staff, Principal)

Role Mission

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.

What You Will Accomplish

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 directly shaped how thousands of patient conversations are powered by our platform.

The Team

You’ll work alongside AI researchers, product managers, clinical experts, and engineers who are obsessed with bringing safe, reliable AI into healthcare. This is a cross-functional team that values both innovation and rigor—people who love solving hard technical problems that have real patient impact, and who move fast without sacrificing safety or quality.

What You’ll Do

• 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
• Prototype and deploy zero-to-one features using state-of-the-art LLMs, retrieval systems, and streaming architectures—balancing rapid innovation with production reliability and clinical safety
• Develop and refine AI-native workflows that support real-time, conversational, and long-running interactions across diverse healthcare contexts, ensuring systems handle edge cases and failure modes gracefully
• Drive continuous improvement in model evaluation, safety testing, and observability—building the measurement and monitoring infrastructure that ensures every agent interaction meets clinical safety standards
• Contribute to technical culture through documentation, knowledge sharing, and collaboration that elevates team capabilities and product quality

Location Requirement

We believe the best ideas happen together. This role is based in our Menlo Park, California office, expected to be five days a week. We’re also exploring establishing a presence in the Bellevue area—if that develops, flexibility on location may be available for exceptional candidates.

Compensation

Compensation is based on experience, expertise, and level of responsibility. We offer competitive packages that reflect the seniority and scope of the role, along with equity, health insurance, and other benefits.

What You Bring

Must Have:

• 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).

Nice-to-Have:

• Experience building or deploying LLM-based or multi-agent systems at scale.
• Hands-on work with speech recognition, text-to-speech, or streaming architectures for real-time AI experiences.
• Prior exposure to healthcare, safety-critical domains, or regulated product development.

If you’re an AI engineer w…