Jobs at Hippocratic AI

Act as an embedded, onsite customer success lead at client health systems to run post-sale delivery, implement and scale AI solutions, train clinical staff, drive adoption, measure outcomes, and collaborate with product, sales, and engineering to improve workflows and patient care.

Lead a regional portfolio of health system partnerships and a team of Directors/senior CS executives. Own executive client relationships, enterprise implementations, change management, and net revenue retention. Build playbooks, drive expansion sales, collaborate with Product/Engineering/Sales, represent the company externally, and support regional hiring and team development. Onsite role in Seattle with occasional travel to Palo Alto.

Drive data-informed decisions through operational reporting, dashboarding, and performance analysis. Split focus on data quality and business reporting, run SQL/Python analyses, build dashboards, produce recurring and ad-hoc reports, and partner with cross-functional GTM and customer teams to surface insights that improve product usage and outcomes.

Serve as an embedded, onsite customer success lead at a health system to drive post-sale delivery, workflow implementation, nursing training, adoption, and measurable clinical outcomes. Build relationships across clinical, operational, and IT teams, manage day-to-day client execution, track success metrics, and relay feedback to product, sales, and engineering.

The Director, Forward Deployed Engineering will lead the technical strategy and integration for deploying AI agents in life sciences, focusing on customer collaboration and complex IT environments. Responsibilities include overseeing technical discovery, development of AI agents, integration architecture with pharma systems, and managing a team of engineers.

Lead technical architect for AI deployments in healthcare: scope customer workflows, design and configure LLM-powered agent workflows, define integration architecture, coordinate engineering, manage projects end-to-end, monitor performance, and drive continuous optimization and knowledge capture to deliver measurable clinical value.

Lead end-to-end AI agent deployments: scope customer workflows, design and configure agent workflows, define integrations, coordinate engineering, monitor performance, and drive optimization and knowledge capture.

The Senior Machine Learning Engineer will design evaluation frameworks, develop synthetic data pipelines, build evaluation systems, and collaborate with teams to improve model quality in healthcare AI.

Act as an on-site, forward-deployed customer success lead for health systems, owning post-sale implementation, workflow discovery, training, adoption, metrics tracking, and stakeholder engagement to drive measurable clinical and operational outcomes.

Lead end-to-end deployments of healthcare-focused AI agents: scope customer workflows, design and configure agents, define and manage integrations, run project timelines, monitor performance, and iterate on analytics-driven optimizations while documenting reusable patterns and advising customers technically.

Lead end-to-end AI agent deployments: scope customer workflows, design and configure agents, define integrations, manage project timelines, monitor post-launch analytics, and drive continuous optimization to deliver measurable clinical value.

Lead end-to-end AI agent deployments: scope customer workflows, design and configure agent workflows, define integration architectures, coordinate engineering builds, manage project timelines, and monitor post-launch analytics to optimize clinical performance and deliver measurable outcomes.

Lead technical design and implementation of AI agent deployments in healthcare: scope customer workflows, design agent configurations, define integrations, manage builds, coordinate engineering, monitor performance, and drive post-launch optimization and knowledge capture to deliver measurable clinical value.

Lead end-to-end AI agent deployments: scope customer workflows, design and configure agent workflows, define integrations, manage engineering coordination, monitor analytics, and optimize post-launch performance to deliver measurable clinical value.

Lead and scale a Customer Success function for enterprise health system partnerships, manage executive client relationships, drive implementations and adoption, define success metrics, lead change management, mentor and grow a team, and collaborate with Product, Engineering, and Sales to translate customer insights into product and go-to-market strategy.

Embedded, onsite customer success lead partnering with clinical, operational, and IT leaders to implement, configure, train, and scale Hippocratic AI solutions. Own post-sale delivery, drive adoption, measure outcomes, improve workflows, present results, and relay client feedback to product, sales, and engineering teams.

The Customer Success Executive will manage relationships with payer organizations, ensuring successful deployment and adoption of AI-driven solutions to improve healthcare outcomes and operational efficiency.

Serve as technical architect for healthcare AI deployments: scope customer workflows, design and configure agent workflows, define integration architecture, lead project execution, monitor post-launch analytics, and drive continuous optimization while documenting reusable patterns.

Design and deliver production-grade healthcare LLM systems: architect advanced RAG and tool-calling solutions, ensure reliability and monitoring across deployments, solve novel AI challenges, mentor engineers, and partner with customer IT for complex integrations.

Technical owner for AI deployments building production Python systems with LangChain/LangSmith and advanced LLM techniques (RAG, tool-calling, LLM-as-judge). Integrates with customer systems, sets up secure monitored deployments, monitors production agents, responds to incidents, and collaborates with customers to ensure reliable clinical AI operations.