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Protege is building a platform to solve AI's biggest unmet need: secure, efficient, and privacy-centric access to training data. The company is backed by world-class investors and powers partnerships with ambitious AI teams.
As an AI Engineer on the Healthcare team, you'll own end-to-end customer engagements from initial feasibility through delivery and post-launch support. This is a hands-on role that bridges customer needs, healthcare data expertise, and platform capabilities.
Key responsibilities include:
- Own healthcare customer engagements end-to-end, serving as the primary technical partner from scoping through implementation, delivery, and support
- Manage multiple concurrent healthcare engagements while maintaining delivery quality and customer confidence
- Develop and deploy custom integrations, data transformations, and applications that solve customer-specific challenges
- Navigate complex customer technical environments and adapt Protege's platform to real-world deployment constraints
- Translate ambiguous customer needs into clear technical plans, milestones, and implementation paths
- Handle healthcare data environments with appropriate care, including PHI and regulatory considerations
- Surface product feedback based on implementation patterns and delivery blockers
- Create reusable playbooks, tooling, templates, and best practices that improve future deal execution
- Partner with healthcare GM, Solutions Leads, and DataLab to navigate customer requirements
Success milestones: By 30 days, you'll support active deals with guidance. By 60 days, you'll independently lead scoped engagements. By 90 days, you'll manage multiple concurrent engagements and contribute reusable assets.
You bring software engineering or solution architecture experience, ideally in forward-deployed or customer-facing technical roles. You're comfortable owning customer engagements end-to-end, executing across integrations and data transformations, and operating in regulated healthcare environments. Strong customer communication, high ownership, adaptability, and bias toward creating reusable systems are essential. The pace is fast, ambiguity is real, and availability outside standard hours is expected when deals are live.
