The Role
We're hiring an Applied AI Engineer to design and ship production agents that synthesize real-time clinical data, surface proactive care recommendations, and take action on behalf of clinicians.
You will take agent architectures from prototype to production in a domain where reliability directly affects patient outcomes. You'll own the full lifecycle of AI-powered clinical workflows: retrieval, reasoning, tool use, evaluation, and safety guardrails. If you've built agentic systems at scale and want your work to matter beyond engagement metrics, we’d love to speak with you.
What You'll Do
- Design, build, and deploy clinical AI agents that reason over patient context, invoke tools, and generate care recommendations.
- Own reliability, observability, and cost efficiency of LLM-powered workflows at scale.
- Build and optimize RAG pipelines over clinical knowledge bases, treatment protocols, and real-time patient data.
- Develop evaluation frameworks: offline benchmarks, safety tests, regression suites, and LLM-as-judge pipelines wired into CI/CD.
- Design multi-step agent orchestration: planning, memory, tool use, error recovery, and human-in-the-loop escalation paths.
- Collaborate with clinical, product, and engineering teams to translate patient care needs into AI system design.
- Stay at the forefront of AI and engineering best practices, continuously pushing the team to raise the bar on quality, performance, and architecture.
What You Need
- Bachelor's or Master’s degree in Computer Science, Engineering or related field, or equivalent work experience
- 3+ years of software engineering experience, with 2+ years building AI/ML-powered systems in production
- Experience in a high-growth, fast-paced environment with end-to-end ownership from design through production
- Hands-on experience with LLM APIs (OpenAI, Anthropic, open-source models) including prompt engineering, tool use / function calling, and structured outputs
- Experience building RAG systems: embeddings, vector stores, retrieval optimization, and grounding
- Experience with agent frameworks or orchestration patterns (tool calling, planners, multi-agent coordination) preferred
- Fine-tuning experience (SFT, RLHF, LoRA) on domain-specific tasks preferred
- Healthcare or regulated-industry experience (HIPAA, SOC 2, clinical data handling) preferred