Applied AI Tech Lead

Posted 3 Days Ago
Be an Early Applicant
New York City, NY, USA
In-Office
240K-300K Annually
Senior level
Artificial Intelligence • Enterprise Web • Healthtech • Software
Building healthcare AI systems organizations stake their reputations on—trust & safety infrastructure for clinical agent
The Role
Lead end-to-end technical delivery of AI deployments for healthcare customers: set architecture, decompose ambiguous problems into deliverable work, own timelines and tradeoffs, mentor engineers, and work directly with customers to ensure production-quality LLM and agent-based systems. On-site in New York City.
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

As an Applied AI Tech Lead at Amigo, you'll lead the technical delivery of customer deployments end to end. You'll set the architecture, break big ambiguous problems into work a team can own, commit the timelines, and hold the bar for what ships. It's a hands-on leadership role at the intersection of engineering, product, and the customer.

Depending on your strengths, the role leans toward deep technical leadership, people leadership, or both. Some tech leads are the deepest technical authority on the team and take the hardest problems themselves. Others manage a handful of engineers and own how the team operates. Most do some of each. We work out the shape with you.

 

What you might work on
  • Leading an AI deployment for a major healthcare customer, from first conversation to production

  • Turning an ambiguous customer need into a clear technical plan with owners and timelines

  • Setting the architecture and engineering standards the team builds within

  • Making the calls on tradeoffs: what to build now, what to reuse, and what to push back on

  • Growing the engineers around you and raising the bar of the whole team

  • Working directly with customers to keep scope, timelines, and quality honest

 

You may be a fit if
  • You've led significant engineering work end to end, as a tech lead, staff engineer, or engineering manager

  • You've broken hard, ambiguous problems into work other engineers could own and deliver

  • You have real experience with LLMs and agent systems, or the depth to get there fast

  • You have the judgment to make tradeoffs and the standing to say no when something doesn't hold up

  • You lead well, whether through architecture, through people, or both

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

  • You can work on site in New York City

 

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

  • Experience shipping production AI or ML systems

  • Background in customer-facing or forward-deployed engineering

  • Experience growing engineers or running a small team

 

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

  • Led significant engineering work end-to-end as a tech lead, staff engineer, or engineering manager
  • Ability to break hard, ambiguous problems into clear, owned work and delivery plans
  • Real experience with LLMs and agent systems or demonstrated ability to develop that depth quickly
  • Judgment to make tradeoffs and the standing to push back or say no when necessary
  • Ability to lead through architecture, people, or both and grow engineers around you
  • Ability to work on site in New York City
  • Experience in a regulated or high-reliability domain (healthcare, finance, legal)
  • Experience shipping production AI or ML systems
  • Background in customer-facing or forward-deployed engineering
  • Experience growing engineers or running a small team
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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