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About This Role
About Crystal PM
Every day, over 10,000 eye care professionals log into Crystal’s software before they see their first patient. We're the operating system behind independent eyecare — the EHR, practice management, billing, scheduling, and payments platform that keeps these practices running smoothly.
With new leadership, fresh growth capital from top\-tier software and search fund investors, and a clear runway to $35mm\+ in ARR, Crystal is at an inflection point. We're not a startup – we're a market leader focused on long\-term value creation. That means building the team, the product, and the go\-to\-market engine to execute on our ambitious goals.
We’re assembling a team that matches the opportunity. If that sounds like your kind of challenge, we'd like to hear from you.
About the role
Crystal’s finance function is seeking someone who combines strong accounting fundamentals with a genuine curiosity about how AI and automation can make financial operations faster, more accurate, and less manual. Reporting directly to the VP of Finance, this position is ideal for someone who wants to grow beyond traditional accounting into finance operations, systems, automation, and long\-term finance leadership. This is not a narrowly scoped accounting role —it’s an opportunity to help build and improve how the finance function operates.
You’ll work across the month\-end close, billing, collections, and financial reporting. You’ll also own audit and tax compliance functions while taking on special projects that span departments: connecting systems that don’t talk to each other, building automated workflows where manual ones exist today, and exploring how AI tools can reduce repetitive work across the finance team. Crystal runs on applications like QuickBooks, Ramp, and Salesforce, and there’s meaningful opportunity to improve how data flows between them.
We’re not expecting you to walk in the door already building AI agents. We are expecting strong accounting skills, curiosity about technology, and the kind of mindset where you look at a manual process and immediately think about how to improve it. You’ll develop the systems and automation side of the role on the job, with support from a finance leader who believes this is where the profession is heading.
Who You Are
- Accounting brain, engineering/systems instinct. You understand debits and credits, and be an engineer today, but you think like one when you look at a workflow.
- A builder. You likely come from an accounting, audit, or finance operations background in an environment with strong technical rigor, but are looking for something more entrepreneurial where you can build, improve systems, and operate with greater ownership.
- Curious and self\-directed. You’re the kind of person who tries the new AI tool before anyone asks you to. You read about what other finance teams are building and you want to do the same. Learning is a habit, not an assignment.
- Detail\-oriented with a systems lens. You catch the small errors that matter, and you also think about how to prevent them at the source rather than catching them every month.
- High agency. When you see something broken, you don’t wait for someone to assign it. You flag it, propose a fix, and move. You’re comfortable with ambiguity and prefer action over perfection.
- Dependable under pressure. Month\-end close gets tight. Customer billing issues come in hot. You stay calm, deliver clean work, and keep commitments.
Qualifications
- 3\-5 years of experience in audit, accounting, financial operations, or a finance\-adjacent role. You have a solid understanding of the month\-end close process, reconciliations, journal entries, and GAAP fundamentals.
- Hands\-on experience with QuickBooks, Ramp, and/or Salesforce preferred. Familiarity with how these systems work and where they fall short is more valuable than certification.
- Genuine curiosity about AI and automation tools. You don’t need to have built production agents, but you should be someone who is actively experimenting: testing new tools, watching what’s possible, and thinking about how it applies to your work.
- Comfort learning new technical skills on the job. Whether it’s connecting systems through APIs, using AI coding tools, or building workflows in AI tooling, you’re willing to figure it out.
- Strong attention to detail and a track record of clean, reliable work product. Finance moves fast here, and accuracy is non\-negotiable.
- Team lead or coordination experience is a plus. Even informal leadership counts if you can point to a time you organized work across people and held them accountable.
- SaaS or healthcare technology experience is a strong plus but not required.
What Success in This Role Looks Like
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- You’ve learned Crystal’s close process, billing infrastructure, and system landscape, and you’re owning the month\-end close. You’re not just executing tasks handed to you. You’re spotting inefficiencies and bringing ideas for how to improve them.
- You’ve mapped how data flows between QuickBooks, Ramp, and Salesforce and identified the manual handoffs that slow things down. You’ve started replacing at least a few of them with automated workflows.
- You’ve started experimenting with AI tools to automate recurring finance tasks. Maybe it’s transaction categorization, maybe it’s a reconciliation that runs itself, maybe it’s a collections follow\-up sequence. The specific build matters less than the pattern: you see a manual process and your instinct is to make it run on its own.
- You’ve taken ownership of audit and tax compliance. You’re managing the annual financial statement audit, preparing monthly and quarterly sales tax returns, and liaising with the external tax team to ensure timely filing of taxes. These are compliance functions that require precision and follow\-through, and you handle them without reminders.
- The VP of Finance trusts your work. Your numbers are clean, your documentation is solid, and you’re becoming the person people come to when they want to understand how something in the financial operations stack works.
Benefits
- Competitive compensation based on experience
- Health, dental and vision benefits
- Hybrid work flexibility with regular in\-office collaboration in Austin; this is a cross\-functional role with a strong in\-person component as someone gets up to speed.
- 401(k) with matching company contribution
- Flexible PTO policy
- Ground\-floor opportunity in a well\-funded, fast\-growing healthcare technology company
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Crystal Practice Management, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Crystal Practice Management AI Hiring
Crystal Practice Management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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