
Applied AI Engineer
AmigoSummary
Amigo is seeking an Applied AI Engineer to design, build, and deploy AI agents for clinical workflows in healthcare. This role offers significant ownership and exposure, working end-to-end on customer deployments. The ideal candidate is comfortable with LLMs and agents, takes ownership, has good judgment, and can turn ambiguous problems into reliable AI solutions. Amigo partners with healthcare organizations to improve patient and provider experiences through AI.
Required Skills
Details
- Salary
- $130,000 – $240,000/yr
- Posted
- Jul 1, 2026
- Equity
- Yes
Description
About Amigo
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.
About this role
Applied AI is how we turn a customer's hardest clinical workflow into production AI that actually ships. As an Applied AI Engineer, you own the technical delivery of real deployments end to end. You design the agents, build them, and are on the hook for whether they work in the field. The team is small and dense, and you get a lot of surface area early.
We hire Applied AI Engineers across every level, from new grads to deeply experienced engineers, and we work out level together once we've met you. What matters is not years on a resume but whether you can take an ambiguous problem and turn it into something reliable that patients and clinicians depend on.
What you might work on
- Designing and building an AI agent for a specific clinical workflow, from first prototype to production
- Deciding what an agent should and should not handle, and where it hands off to a human
- Building the evaluations that decide whether an agent is safe to ship, and catching failure modes before patients do
- Writing the integrations that let agents work inside a customer's existing systems
- Debugging why a live conversation went wrong and fixing it across the whole stack
- Finding the patterns that repeat across customers and turning them into something reusable
You may be a fit if
- You've built real software that other people relied on, and you care whether it actually works, not just whether it shipped
- You're comfortable with LLMs and agents, or you'll get comfortable fast
- You take ownership. When something is broken you fix it, and you say so early when something is more than you can carry
- You have good judgment about tradeoffs, and you'd rather get the direction right than move fast in the wrong one
- You're low ego, direct, and hold yourself to a high bar
- You can work on site in San Francisco
This role spans new grads through senior and staff engineers. Strong early-career people are welcome, and so are people who have done this work for a long time.
Nice to have
- Experience in a regulated or high-reliability domain (healthcare, finance, legal)
- Background with evaluation, simulation, or synthetic data
- Experience working directly with customers or domain experts
- Python depth and experience building reliable systems on top of external APIs
Benefits (available to Full-Time Employees)
Health & Wellness
- 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
Growth & Development
- Annual learning budget for courses, books, or conferences
- Conference attendance budget for professional development
- Annual team offsite
- Academic collaboration opportunities
- Unlimited PTO
Our Core Values
- 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.
- 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.
- 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.
- 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.
Who Builds With Us
- 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.
