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Amigo is seeking an Applied AI Engineer (Pre-Sale) to join their team. This role is at the intersection of research, product, and sales, focusing on building demos, prototypes, and proofs to showcase AI capabilities to prospects. You will act as the experimentation engine for go-to-market, rapidly developing solutions for complex clinical workflow challenges. The role is open to all levels, from new graduates to experienced engineers, with a focus on the ability to turn ambiguous problems into working prototypes quickly. Experience with LLMs and agents is preferred, and a background in healthcare or sales engineering is a plus.
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.
This is a GTM engineering role at the frontier of applied AI. You sit where research, product, and revenue meet. You take the newest models and our platform and build the demos, prototypes, and proofs that make prospects believe what's coming, and then help close the deal. Every prospect is a research question: what can an agent do in this clinical workflow that nobody has tried yet? You go find out, in code, fast enough to shape a live conversation with a customer.
Think of yourself as the experimentation engine for go-to-market. When a prospect describes a hard problem, you disappear for an afternoon and come back with something working. You run ahead of the roadmap, probe what the platform can do in new clinical domains, and turn the most promising experiments into repeatable plays the whole GTM org can run.
We hire 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 turn an ambiguous problem into a working prototype fast enough to change someone's mind.
This role spans new grads through senior engineers. Strong early-career people are welcome, and so are people who have done this kind of work for a long time.
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.
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.
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.
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.
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.