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About This Role
Company Overview
Austin Eye is a leading eye care and surgical practice dedicated to providing exceptional surgical vision correction, oculoplastics surgery and eye health services. Established in 1969, the organization boasts a team of highly skilled ophthalmologists, optometrists, and support staff across multiple locations, committed to delivering innovative and compassionate care to enhance patients' quality of life.
Job Summary
We are seeking a hands-on professional who combines deep knowledge of medical billing, coding, and revenue cycle operations with practical AI and automation skills. You will design, implement, and optimize AI-assisted and automated workflows that support prior authorizations/precertifications, billing and coding accuracy reviews, and insurance payment posting—working directly with our existing practice management and EHR software.
This is a high-impact individual contributor role focused on reducing manual work, improving accuracy and turnaround times, and increasing clean claim rates and collections while maintaining compliance and appropriate human oversight for complex or exception cases.
Key Responsibilities
- Analyze current prior authorization, coding/billing review, and payment posting processes and identify high-volume, rule-based tasks suitable for automation or AI assistance.
- Design and implement AI-enabled and automation solutions (including RPA bots, AI agents, LLM-based tools, scripts, or low-code platforms) that integrate with our existing systems and payer portals.
- Build or configure tools that assist with prior authorization packet assembly, status tracking, documentation review, coding accuracy checks (ICD-10, CPT, HCPCS), claim scrubbing, and insurance payment posting/reconciliation.
- Monitor performance, refine prompts/workflows/rules, handle exceptions, and escalate cases requiring clinical or complex judgment.
- Collaborate with billing staff, coders, and providers to ensure workflows are accurate, compliant, and practical.
- Maintain audit trails, support HIPAA compliance, and document processes for reliability and scalability.
- Identify opportunities to expand automation across other revenue cycle functions as results are proven.
Required Qualifications
- 3+ years of experience in healthcare revenue cycle management, medical billing, coding, prior authorizations, or closely related operations.
- Hands-on experience applying automation tools (RPA such as UiPath, Selenium, or similar; workflow tools; or custom scripting) and/or AI technologies (LLMs, AI agents, prompt engineering, or NLP) to real business processes.
- Working knowledge of ICD-10, CPT, HCPCS, payer rules, claims processes, EOBs/ERAs, and common practice management/EHR systems.
- Ability to integrate solutions with existing software via APIs, EDI, portals, or other practical methods without requiring a full system replacement.
- Strong problem-solving skills, attention to detail, and ability to translate operational needs into reliable workflows.
- Understanding of HIPAA and healthcare compliance requirements.
Preferred Qualifications
- Bachelor’s or Master’s degree in Health Informatics, Computer Science, Data Science, Health Information Management, Healthcare Administration, or related field (or equivalent practical experience + certifications).
- Certifications such as CPC, CCS, RHIT/RHIA, or specialized AI-in-RCM or RPA credentials.
- Experience with agentic AI frameworks, document extraction, payment posting automation, or prior authorization workflows.
- Demonstrated results improving efficiency, accuracy, or collections through automation or AI tools.
- Familiarity with our electronic health record and practice management software: Sightview software/Imedicware.
What Success Looks Like
- Measurable reduction in time spent on routine prior authorizations, coding reviews, and payment posting.
- Higher accuracy and fewer avoidable denials or rework.
- Reliable, documented workflows that staff can trust and oversee.
- Clear ROI through improved cash flow and reduced administrative burden.
To Apply: Please submit your resume and a brief note describing a relevant automation or AI project you’ve implemented (or a workflow you would target first in a medical office setting). Include any examples of working with healthcare systems or revenue cycle processes.
Pay: $51,400.00 - $100,000.00 per year
Benefits:
- 401(k) matching
- Employee discount
- Health insurance
- Retirement plan
Work Location: In person
Salary Context
This $51K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Austin Eye, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($75K) sits 64% below the category median. Disclosed range: $51K to $100K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Austin Eye AI Hiring
Austin Eye has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $100K - $100K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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