Staff Software Engineer - Backend/Infra [NYC or SF]

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 a Staff Backend/Infra Engineer at Amigo, you'll build the core services behind our agent platform. The systems you own handle millions of conversations a month, so concurrency, reliability, and clean design are the job itself, not an afterthought. You'll own services end to end, from design through production.

• Designing high-throughput services that handle concurrent conversations at scale

• Owning a core platform service from design to production

• Building systems that stay available and degrade gracefully when things fail

• Handling sensitive healthcare data with the care it requires

• Improving performance, observability, and operational health across services

• Shaping architectural decisions as the platform grows

• Think carefully and holistically about system design under time pressure.

• You've built and operated production backend systems at scale and owned the outcome

• You're strong with concurrency, distributed systems, and service design

• You prefer simple, maintainable solutions to hard problems

• You debug well across services, queues, and databases

• You're low ego, direct, and hold yourself to a high bar

• You can work on site in New York City or San Francisco

• Experience in a regulated or high-reliability domain (healthcare, finance, legal)

• High-throughput or low-latency system design

• Experience with production AI or ML systems

• 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.