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About This Role
AI Engineer
Healthcare Retroactive Audits, Inc. (HRA)
Pay: $90,000 – $105,000 per year
Location: Fully remote (United States)
Job type: Full\-time
About HRA
We are a healthcare revenue cycle firm. We recover money that hospitals and health systems are owed and did not collect.
A few things that are true about us and are not true about most companies hiring for this title:
We have been profitable for over twenty years and we carry no debt. Nobody here is building toward a funding round.
We are small. You will report directly to our CIO, not to a manager who reports to a director who reports to a VP. Your work will be in production and in use by people you know by name.
The AI work is already running. This is not a pilot, a proof of concept, or a slide in a strategy deck. There is a real workstream here, currently owned by someone who is leaving, and we are looking for the person who will own it next.
If you want to be the ninth engineer on a platform team at a company that talks about AI, this is not that. If you want to be the person who builds it, it might be.
What you'll do
● Design, build, and maintain production AI tooling that turns unstructured documents into structured, verifiable data.
● Write the Python that moves data between source systems, applications, and SQL Server, and keep it running.
● Build LLM\-powered document generation from structured inputs.
● Bring engineering discipline to AI in a regulated environment: validation against source data, guardrails, monitoring, and the habit of verifying before anything reaches production.
● Work with internal teams to find what should be automated, and be honest about what shouldn't.
● Document what you build so that it outlives you.
*This is a build role. You will ship.*
What we need
● 3\+ years writing production Python. Not notebooks. Code that other people depend on.
● 2\+ years of SQL. You are comfortable in a relational database and can reason about a query that spans several tables.
● Hands\-on experience building with an LLM API — Claude, or an equivalent such as OpenAI, Gemini, or Bedrock. Specifically: prompt design, structured JSON output, document and PDF inputs, and handling multi\-block responses. We use the Claude API. If you have shipped against a comparable API, the skills transfer and we will say so out loud.
● Experience parsing documents and PDFs into structured data, and knowing what to do when the parse is wrong.
● The judgment to know when an AI system is not ready for production, and the willingness to say so.
● Clear written communication. You will explain technical decisions to people who are not engineers, including the CEO.
What helps
● T\-SQL and Microsoft SQL Server specifically: stored procedures, schema design, query tuning.
● Experience with agentic workflows — orchestration, state management, validating agent output against source data.
● Prior work in healthcare, finance, or another regulated data environment.
● Familiarity with HIPAA and PHI handling. If you have not worked under HIPAA before, we will train you. You do need to care about it.
● Azure.
● Git and a real development\-to\-production release process.
*We would rather hire someone strong on Python, SQL, and LLM APIs who can learn our domain than someone who checks every box and cannot build.*
Compensation and benefits
● $90,000 – $105,000 annually, depending on experience.
● Fully remote, anywhere in the United States.
● Health coverage through our Individual Coverage HRA
● 401(k) with company match.
● Professional development support, including healthcare revenue cycle and HIPAA compliance training.
How to apply
Send your resume. In addition, we ask every applicant to answer two questions. Answer them yourself, briefly and specifically — we read them first, before the résumé.
1\. Link us to something you built: a repository, a deployed system, or a technical write\-up. Tell us in one sentence what it does.
2\. Describe a specific time an LLM system you deployed produced output that was confidently wrong. What was the failure mode, and what guardrail did you add?
We are far more interested in a short, concrete answer than a polished, general one.
*Healthcare Retroactive Audits, Inc. is an Equal Opportunity Employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other protected characteristic.*
Pay: $98,698\.53 \- $118,862\.75 per year
Benefits:
- 401(k)
Work Location: Remote
Salary Context
This $98K-$118K range is in the lower quartile 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 Healthcare Retroactive Audits, Inc, 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 ($108K) sits 50% below the category median. Disclosed range: $98K to $118K.
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.
Healthcare Retroactive Audits, Inc AI Hiring
Healthcare Retroactive Audits, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $118K - $118K.
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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