Staff AI Engineer

About Sage Care

Sage Care is a fast-growing, early-stage healthcare startup founded by exceptional leaders from Apple, Uber, Carbon Health and backed by top-tier venture capital (General Catalyst, Chelsea Clinton). With a strong customer pipeline, Sage Care is transforming healthcare by simplifying care navigation.

Our platform makes it easier for patients to find the right doctor, helps providers focus on those who need them most, and ensures faster access to care—delivering better care and stronger economic outcomes at scale through harnessing the latest AI innovations.

Building on our successful collaborations with health systems across the U.S., we have expanded internationally to the MENA region. We are now partnering with health systems there to deploy our AI-powered care navigation platform.

About the Role

We’re hiring a Staff AI Engineer to lead the design and evolution of Sage Care’s AI agents, with a focus on voice systems.

This role focuses on building and operating AI agents that perform reliably in real-world conditions. You’ll own how our agents behave end-to-end — from orchestration and tool use to latency, evaluation, and failure handling.

You’ll work on complex, non-deterministic systems and design the architecture needed to make them consistent, observable, and scalable in production.

What You’ll Do

• Design and own AI agent architecture (orchestration, tool use, control flow)
• Build systems that enable agents to operate reliably across real-world interactions
• Develop and improve evaluation frameworks for agent quality and behavior
• Debug and resolve complex issues in production (edge cases, failures, regressions)
• Optimize performance across the stack (latency, cost, reliability)
• Define patterns and best practices for building and operating AI agents
• Mentor engineers and raise the bar on AI systems design and execution

What We’re Looking For

Required

• 7+ years of software engineering experience
• Experience building and operating AI agents or conversational systems in production
• Strong understanding of:
• agent orchestration and tool use
• multi-step workflows and control flow
• LLM behavior in real-world settings
• Experience designing evaluation systems for AI quality
• Strong debugging skills across complex, non-deterministic systems

Nice to Have

• Experience with voice systems (speech-to-text, text-to-speech, real-time pipelines)
• Experience building customer support agents or similar systems
• Experience optimizing low-latency / real-time AI systems
• Experience working with multiple model providers or inference stacks

What Success Looks Like

• Agents that handle real-world interactions reliably and predictably
• Clear, scalable agent architecture (not ad-hoc or fragile systems)
• Strong evaluation loops that continuously improve agent quality
• Fast identification and resolution of production issues
• Systems that balance flexibility (AI) with control (engineering)