Applied AI Engineer

Reposted 2 Days Ago
New York City, NY, USA
In-Office
130K-240K Annually
Junior
Artificial Intelligence • Enterprise Web • Healthtech • Software
Building healthcare AI systems organizations stake their reputations on—trust & safety infrastructure for clinical agent
The Role
Build, ship, and validate production AI agents for healthcare: implement context graphs, agent configurations, OPDs, verification and adversarial testing, integrate tools via Python/APIs, collaborate with customers, and systematize deployment and evaluation frameworks to meet clinical KPIs and reliability requirements.
Summary Generated by Built In
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 New York City

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
  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.

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.

Skills Required

  • 2+ years of production software engineering experience
  • Strong Python skills, including proper use of typing
  • Experience building highly reliable systems that interact with external APIs (schema design, retry strategies, error propagation)
  • Experience with LLMs, prompt engineering, and building on AI platforms
  • Ability to implement systems with strict reliability requirements
  • Understanding of testing and verification methodologies
  • Experience working directly with customers or stakeholders to deliver technical solutions
  • Familiarity with git workflows and collaborative development
  • Strong debugging and problem-solving skills for complex systems
  • Clear technical communication for both engineering and non-technical audiences
  • Experience in regulated industries (healthcare, finance, legal)
  • Background with simulation, synthetic data, or evaluation frameworks
  • Understanding of distributed systems and service-oriented architectures
  • Experience with observability and monitoring in production environments
  • Familiarity with compliance requirements (HIPAA, SOC 2, GDPR)
Am I A Good Fit?
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The Company
HQ: New York, New York
30 Employees
Year Founded: 2024

What We Do

Amigo AI builds trust and safety infrastructure for clinical agents—ensuring AI systems in healthcare provide quantified confidence when mistakes aren't an option. Our platform combines advanced simulation, verification, and recursive optimization to enable healthcare organizations to deploy AI with statistical guarantees about its behavior. We solve the fundamental challenge of reliable AI in critical domains through deterministic verification for clinical protocols and continuous drift detection for real-world performance. Our systems provide complete transparency—every AI decision is traceable and auditable, with quantified confidence intervals rather than black box predictions. Founded by technologists from Google, Meta AI, Databricks, Coda, and Plaid, we've built systems that let organizations make informed risk decisions about AI deployment in healthcare. Our interdisciplinary approach draws from computer science, economics, physics, and mathematics to tackle human-centric optimization problems where people and populations are at the center of every solution. We're actively working with healthcare organizations across digital health, cancer care, cardiac care, and personalized medicine to deploy AI systems that continuously learn and adapt from real-world feedback while maintaining verified safety boundaries. Our technology amplifies human expertise rather than replacing it, empowering domain experts to achieve outcomes neither could accomplish alone.

Why Work With Us

We build AI healthcare systems where 99% isn't good enough. Rapid growth—promotions in 3 months. Freedom to work your way: art museums or late nights. Tackle recursive optimization problems that ship to production. Your work directly impacts critical healthcare decisions. Diverse team from Google, Meta AI, Databricks solving problems that matter.

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