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About This Role
The Problem
Severe mental illness affects over 15 million adults in the United States. Schizophrenia. Schizoaffective disorder. Bipolar I. Treatment\-resistant depression. These are the conditions the rest of healthcare has systematically failed to build for.
What makes this problem different from most of healthcare is that the tools to solve it do not exist. The clinical protocols, the measurement science, the precision medicine, the technology to deliver coordinated longitudinal outpatient care for this population. None of it has been built. This is a frontier problem, and it requires frontier science and care delivery to solve.
We are developing all of it. The care, the science, the technology.
### Who We Are
Severe mental illness affects over 15 million adults in the United States. Schizophrenia. Schizoaffective disorder. Bipolar I. Treatment\-resistant depression. These are the conditions the rest of healthcare has systematically failed to build for.
What makes this problem different from most of healthcare is that the tools to solve it do not exist. The clinical protocols, the measurement science, the precision medicine, the technology to deliver coordinated longitudinal outpatient care for this population. None of it has been built. This is a frontier problem, and it requires frontier science and care delivery to solve.
We are developing all of it. The care, the science, the technology.
### The Role
Engineering excellence drives everything we do, as well as how we scale care.
As our Senior AI\-Native Engineering Manager, you'll be a systematic thinker about how emerging AI\-driven development practices can reshape our entire lifecycle. We don't just use AI; we build our product development processes around it, leveraging AI\-native workflows to accelerate architecture, automate testing, and achieve solid reliability.
You'll lead a focused engineering team building AmaeCare, our product enabling clinicians to provide better care, and AmaeBetter, our mobile app that motivates patients to engage with their care. You won't just ship features; you'll own the reliability, velocity, and architecture of both products end\-to\-end.
This is a high\-ownership role at the intersection of product and engineering. You'll partner directly with our CPO on roadmap and requirements, bringing engineering thinking into strategy while translating product vision into technical reality.
### What You'll Do
#### AI\-Native Engineering
- Architect and scale AI\-native workflows that define our standard development lifecycle.
- Institutionalize AI\-first development: integrate automated code generation, AI\-assisted architecture reviews, and self\-healing test suites into our daily engineering operations.
- Further the evolution of our engineering culture to be AI\-native, continuously measuring and optimizing the impact of AI tools on velocity and code quality.
#### Engineering Strategy
- Own end\-to\-end engineering delivery and reliability for AmaeCare and AmaeBetter.
- Architect shared systems in collaboration with senior engineers to ensure scale.
- Drive continuous improvement in systems and processes, enforcing a high bar for production quality while balancing velocity.
#### Product Strategy
- Partner with the CPO on product roadmap, requirements, and technical vision.
- Collaborate with the Care Delivery team to inform product decisions and ensure alignment with clinical needs.
- Translate clinical and user feedback into actionable technical strategy.
#### Team Leadership
- Lead a focused engineering team with clarity, mentorship, and high standards.
- Set the tone for quality, ownership, and product thinking.
- Foster a culture where engineers understand the clinical impact of their work.
### What We're Looking For
- Extensive experience as a Senior or Staff level software engineer with deep architectural expertise and ability to drive large\-scale technical initiatives.
- 3\-5 years of leadership (management and/or lead) experience
- Deep fluency in AI\-native development systems and experience building agentic development systems, innovating on systematic, technical improvements to the full development lifecycle.
- Proven track record of leading teams that ship reliable, high\-quality user\-facing products in fast\-paced startup environments by balancing velocity and reliability without sacrificing either.
- Product mindset. You think like a product person and can help shape requirements (Product management experience is a plus)
- Strong communication and influence across technical and non\-technical stakeholders: you can carry a room, balance competing input, and push initiatives forward with urgency
- Deep full\-stack proficiency (React, Next.js, TypeScript, Node.js, SQL/PostgreSQL) with hands\-on experience deploying and managing applications on cloud platforms like Vercel or AWS.
- Solid understanding of security, authentication/authorization, and data privacy best practices
- Familiarity with FHIR, HL7, and healthcare interoperability standards (nice\-to\-have)
- Degree in Computer Science or equivalent experience
- Able to work in\-person from San Francisco or New York
### Why This Role Matters
Every line of code we ship impacts patient outcomes and clinician effectiveness.
If we get this right:
- Clinicians have tools that work reliably, every time
- Patients receive care powered by technology that understands their needs
- We move faster without sacrificing stability
You're not just managing engineers; you're building the technical backbone of a system that changes lives.
### Compensation \& Benefits
Amae Health is committed to fair and equitable compensation practices. Base salary range for this role is $195,000 \- $235,000 per year, and will be adjusted to market cost. Comprehensive medical, dental, and vision coverage. Parental leave and programs built around well\-being. We'll share specifics during the interview process.
*Amae Health is building the care platform, the precision medicine, and the frontier science that severe mental illness has never had. If you want to be part of creating something this hard and this important, something that has never been done, we'd like to talk.*
What We Value
- We center care in all we do. Empathy is not a brand value. It's how we make clinical decisions, build products, and treat each other.
- We challenge convention. The existing system is the problem. We question it, we test alternatives, and we move with urgency when something works.
- We take the work seriously, not ourselves. High standards and humanity are not in tension. We hold a hard bar for quality while leaving room for humor and levity.
- Your job isn't done until the job is done. We close gaps, we follow through, and we don't hide behind titles or org charts.
- We win together and fail together. We own outcomes as a team. We learn fast. We don't do blame.
- We hustle with humility. Speed matters. So does integrity. We assume best intent and stay grounded in the mission.
Salary Context
This $195K-$235K range is above 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 Amae Health, 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. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $195K to $235K.
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.
Amae Health AI Hiring
Amae Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $235K - $235K.
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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