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About This Role
About Eliza
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We are a technology services company dedicated to helping organizations build and deploy cutting\-edge AI solutions. As an official OpenAI partner, we work side\-by\-side with enterprise clients to design, build, and deploy generative AI, custom LLM integrations, intelligent automation, multi\-agent orchestration, AI software development lifecycle, and AI\-powered decision systems. We embed AI into teams, workflows, and systems — then we get out of the way.
We're 40 people big, working remotely across the US with an NYC\-based office, seed\-stage funded, and growing incredibly fast as Enterprise clients invest significantly in the acceleration of AI solutions for various operational units within their companies. Every person on our team is a founding\-team member in practice — close to the work, close to clients, and close to the decisions that shape this company.
About This Role
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This role exists to build AI solutions for a healthcare system under pressure to evolve its core operating rhythms to meet today's landscape of AI capabilities. From delivering care to managing compliance to processing payments, there's a significant opportunity to transform how healthcare fundamentally operates. Specifically, we're going after a massive inefficiency and error problem in the industry: claims accuracy and decision velocity — the messy, high\-stakes workflow layer where revenue cycle, clinical operations, and payer rules collide.
Getting this right takes someone who has lived in that mess. You know how a hospital actually runs — how claims move, where denials come from, how Epic is really used on the floor versus how it's drawn up on a slide. And you can pair that operational credibility with clear\-eyed AI judgment: knowing what can be automated safely today, what demands real integration work, and where the risk sits before it becomes a client's problem.
This is also a role that sells and shapes, not one that advises from the sidelines. You'll be in the room with senior healthcare buyers — CFOs, revenue\-cycle and clinical leaders — helping them see what's possible, scoping the work, and owning the engagement from first conversation through delivered outcome.
This is a rare opportunity to join Eliza at a pivotal stage — when the company is small enough that you'll have real impact and the market is moving fast enough that the work genuinely matters. You'll be operating at the frontier of one of the most consequential technology shifts of our time, in one of the most important industries: healthcare.
What You'll Own
- Design and build Eliza's new healthcare practice from the ground up — while continuously improving how Eliza delivers, working with cross\-functional teams of AI Product Managers and Forward Deployed Engineers.
- Shape and sell solutions with senior healthcare buyers: partnering with CFOs, revenue\-cycle, and clinical leaders to define the problem, scope the work, and win the engagement — not just advise once it's sold.
- Make the practical AI delivery calls — what to automate now, what needs deeper integration, and where the clinical, compliance, and financial risk sits — and translate those calls into clear, compelling narratives clients trust.
- Own the full engagement arc from SOW creation to solutions delivery: challenge the status quo, experiment boldly, and bring new thinking to messy, real\-world healthcare workflows.
- Manage competing priorities across the new healthcare practice and the broader business, without losing sight of what matters most.
How We'll Know You're Crushing It
- You've built the foundation for Eliza's healthcare vertical practice from 0\-to\-1 in your first months.
- You've helped establish Eliza as a widely known authority in AI professional services for healthcare.
- You've delivered a handful of successful AI implementations across major healthcare client engagements, with measurable outcomes we can build on.
What You'll Bring
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- Deep healthcare ops credibility. 8\+ years working inside complex healthcare implementations for large clients (at least 2 in a leadership role), with real fluency in revenue cycle management (RCM), claims and denials, and the messy workflow layer where clinical, financial, and payer systems meet. You understand how hospitals and insurers actually operate — not just the org chart.
- Epic fluency. Hands\-on experience with Epic in production environments; bonus if you're certified in Resolute HB/PB and/or Tapestry. Equivalent depth in another major EHR/health\-system platform considered.
- Ability to sell and shape solutions with senior buyers. A track record of partnering with senior healthcare stakeholders (CFOs, revenue\-cycle and clinical leaders) to scope, shape, and win work — owning the commercial arc from first conversation to signed engagement, not advising from the side.
- Practical AI delivery judgment. Strong instincts for the right tool for the right use case, plus familiarity with LLMs, RAG, agentic/multi\-agent frameworks, and prompt engineering. You can tell what's safely automatable today, what requires real integration work, and where the risk sits — and you make that call before it becomes a problem.
- HIPAA, regulatory, and data\-security knowledge. Fluency with the compliance and data\-protection realities of building in a regulated healthcare environment.
- Interoperability familiarity. Working knowledge of HL7 / FHIR and how healthcare data actually moves between systems.
Core Strengths
- Optimizes Work Processes — you're a systems thinker who constantly finds ways to do things better
- Communicates Effectively — you're an exceptional communicator across formats — written, verbal, and executive\-facing
- Cultivates Innovation — you're a creative, first\-principles thinker who challenges conventional approaches
- Manages Complexity — you navigate and simplify multi\-dimensional, fast\-moving challenges with ease
- Balances Stakeholders — you manage competing interests without losing sight of what matters most and earn credibility with clinical and revenue\-cycle leaders
Why Leave Your Current Role for This One
This is the opportunity to revolutionize how health care is delivered in this country, saving millions of dollars and most importantly, ensuring better experiences for patients, and it’s a de\-risked bet; We’re more than doubling revenue every quarter.
What We Offer
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- Equity in a seed\-stage company on a steep growth trajectory in one of the most important technology markets ever created
- A seat at the table — real influence over how this company is built
- Competitive base compensation with performance\-based incentives tied to outcomes, including healthcare coverage and retirement investment plans
- WFH stipend for home office setup
- The chance to work on genuinely novel problems with healthcare organizations navigating real AI transformation
- A high\-caliber team that moves fast, trusts each other, and cares deeply about the work
- Flexibility in how and where you work, with opportunities for on\-site client engagement when meaningful
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 Eliza, 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.
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.
Eliza AI Hiring
Eliza has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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