Applied AI Engineer II

Amigo partners with healthcare organizations to deploy robust AI infrastructure that directly serves patients and providers. Our agents handle clinical workflows and patient engagement across the entire journey: pre-visit intake, care navigation, post-visit care plans, patient monitoring, and more.

We're fresh off our Series A backed by Tier 1 investors like Madrona, General Catalyst, and Optum Ventures. Our work is validated with leading academic medical institutions. Our agents have reached 3M+ patient encounters and are on track to 10x this year.

As an Agent Engineer II at Amigo, you'll independently design and implement production AI agents for healthcare customers. You'll architect context graphs that model complex clinical workflows, design agent personalities that maintain clinical safety, and build evaluation frameworks that catch problems before patients encounter them. This role requires you to make design tradeoffs -- balancing conversation quality, clinical safety, and system reliability -- with minimal oversight.

• Design context graphs (hierarchical state machines) that model multi-step clinical workflows -- choosing between linear arcs and routing hubs, calibrating state density, and preventing conversation loops

• Architect agent identities: background, motivations, expertise, behaviors, and communication patterns that produce clinically safe and engaging conversations

• Build dynamic behavior sets that inject contextual instructions at runtime -- designing trigger conditions, choosing override modes, and testing activation patterns

• Design user memory systems by defining extraction dimensions that are bounded, orthogonal, and actionable -- preventing the dimension overlap and storage explosion that collapse memory systems

• Write tool integration specs that define when tools fire, what parameters they receive, and how results persist in conversation context

• Diagnose production conversation failures by reading prompt logs, tracing routing decisions, and identifying root causes across the agent-graph-behavior stack

• Design evaluation suites: metrics that resist gaming (Goodhart's Law), personas that represent real patient populations, and scenarios that test edge cases

• Run coverage-optimized simulations using frontier and heatmap algorithms to systematically test all reachable states and transitions

• Process complex customer feedback -- categorizing issues into agent design problems, context graph flow issues, platform bugs, and knowledge gaps

• 2-4 years of production software engineering experience

• Strong Python skills including Pydantic models, async patterns, and building reliable systems that interact with external APIs

• Experience with LLMs, prompt engineering, or building on AI platforms

• Ability to design systems by reasoning about competing constraints -- you understand that boundary constraints matter more than action guidelines, and that quality trumps speed

• Experience working directly with customers or domain experts to translate requirements into technical implementations

• Debugging skills across multiple system layers -- you can trace a problem from user-visible symptom to root cause across logs, prompts, and configuration

• Understanding of testing methodologies -- you think about what to measure, not just whether tests pass

• Clear technical communication for both engineering and clinical audiences

• Experience in regulated industries (healthcare, finance, legal)

• Background with state machine design, finite automata, or conversation flow modeling

• Experience with simulation frameworks or synthetic data generation

• Understanding of distributed systems and observability (Datadog, structured logging)

• Familiarity with compliance requirements (HIPAA, SOC 2)

• Experience with voice/TTS systems and audio-specific constraints

• Comprehensive health, dental, and vision insurance

• Daily catered lunch and dinner

• Mental health support and wellness coaching

• Flexible wellness stipend for fitness, therapy, or personal growth

• Annual learning budget for courses, books, or conferences

• Conference attendance budget for professional development

• Annual team offsite

• Academic collaboration opportunities

• Unlimited PTO

1. Patients Win, We Win

   If patients aren't getting better care, we haven't earned the right to scale. Every internal decision gets pressure-tested: does this make patients' lives better? If we can't draw the line, we question why we're doing it.

2. High Standards, High Care

   We hold a high bar for the team because patients are counting on us to get this right. But high standards only work with genuine investment in each other. You can take risks, admit mistakes, and challenge ideas—not despite our standards, but because of them.

3. Thoughtful Urgency

   We move fast by default, but speed without judgment is recklessness. The discipline is knowing which decisions are reversible vs. not. In healthcare AI, the companies that win will be fast everywhere they can be and careful everywhere they must be. We build the muscle to do both.

4. Intensely Measured

   We instrument patient outcomes, provider ROI, system performance, and clinical accuracy. But data without action is surveillance. Every metric should have an owner, a threshold, and a response plan. If we're measuring something but never acting on it, we stop measuring it.

• Low ego: Politics and territory don't interest you. The best ideas win, regardless of who has them.

• Direct: You say the hard thing, challenge ideas openly, and commit fully once decided.

• High agency: You thrive on trust rather than instruction. When you see something is broken, you fix it. You don’t file tickets and wait for someone else.

• Bar of excellence: You hold yourself to a bar most people wouldn't, and you want teammates who do the same.

• Skeptical: You push back on rules that don’t make sense and question assumptions that haven’t earned their place.