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About This Role
About Amigo
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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 (Pre\-Sale) is how we show a customer what production AI can do for their hardest clinical workflow before they commit. You take a vague problem, build a working prototype or a live demo, and put something real in front of them quickly. You partner with account executives on technical discovery, scope what we can build, and give prospects the confidence to move forward.
The work is equal parts experimentation and research. Each prospect is a question about what's achievable in a clinical domain we haven't tackled yet, and you answer it in code. You track what new models make possible the week they ship, test them against real customer problems, and learn what holds up before it reaches a demo.
The role exists so our delivery engineers can stay focused on production deployments while you own the pre\-sales technical surface. You sit at the intersection of sales and engineering: close enough to the product to build on it, curious enough to push it into new territory.
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 something working, fast.
What you might work on
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- Building rapid POCs and demo environments tailored to specific prospect workflows and clinical use cases
- Running technical discovery with account executives to scope what's possible and shape deal architecture
- Designing and delivering live technical demonstrations that turn prospect pain points into working agent experiences
- Prototyping experimental agent configurations that explore new use cases and push the boundaries of what our platform can do
- Researching what new models and techniques make possible, testing them against real customer problems, and separating what works from what only demos well
- Running technical deep dives with prospect clinical, IT, and operations teams to understand integration requirements and workflow constraints
- Creating reusable demo assets, reference architectures, and POC templates that accelerate future sales cycles
- Turning technical objections into solutions during the sales process, from security to compliance to integration feasibility
- Handing won deals cleanly to the delivery team, documenting POC learnings and customer expectations
- Feeding insights from the field back to product and engineering: what prospects ask for, what patterns emerge, and where the platform should go next
You may be a fit if
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- You have strong hands\-on coding ability and can build a working prototype, not just talk about one. Python proficiency matters here
- You have experience with LLMs, prompt engineering, and building on AI platforms, or you'll get there fast
- You have a hacker mentality: fast, scrappy, and energized by building things from scratch under time pressure
- You're genuinely excited by AI experimentation and novel use cases, and you follow the space closely
- You can run a room, present to technical and executive audiences, handle objections live, and think on your feet
- You have strong discovery skills and ask the questions that uncover the real problem, not just the stated one
- You're comfortable with ambiguity. Prospects don't hand you clean requirements, and you do your best work when they don't
- You're low ego, direct, and hold yourself to a high bar
- You can work on site in New York City
Nice to have
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- Experience in healthcare technology or regulated industries
- A background moving between engineering and customer\-facing work in either direction
- Familiarity with healthcare workflows, EHR systems, or clinical terminology
- Experience building demo environments or sandbox platforms
- Understanding of healthcare compliance requirements (HIPAA, SOC 2\)
- A track record of influencing deal outcomes through technical work
Benefits (available to Full\-Time Employees)
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Health \& Wellness
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- 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
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- 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
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- 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.
Compensation Range: $130K \- $200K
Salary Context
This $130K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At AMIGO, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($165K) sits 25% below the category median. Disclosed range: $130K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
AMIGO AI Hiring
AMIGO has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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