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About This Role
Why Aiola CPA, PLLC
- Specialized Focus – We work exclusively with real estate investors. You'll be solving real problems for a specific audience, and you'll get very good at it.
- Fully Remote – We've been fully virtual from day one. Work from wherever you do your best work.
- Autonomy – You'll have ownership over what you build and how you build it. We set clear goals and trust you to get there.
- Growth Opportunity – This is a ground\-floor role on our technology side. We're investing heavily in AI and internal software, and this role has a direct path into firm leadership.
- People\-First Culture – Unlimited PTO, flexible scheduling, hobby days, and team trips and trainings. We genuinely value the people on this team.
Role Overview
Aiola CPA is a virtual CPA firm serving real estate investors across the US. Our goal is to be a leader in our niche. AI is changing the landscape of professional services, and we want to make sure we are out in front of it as leaders in the space.
This role is about helping us build for what comes next. You will design and maintain the internal software apps and AI systems that make us better, more efficient, and more valuable to our clients. That includes replacing an expensive SaaS tech stack with customized tools we actually build for our specific purposes. But the bigger opportunity is using AI to push the firm forward and redefine what advisory, tax, and accounting services look like entirely.
One non\-negotiable for us is the importance of security. We handle sensitive data like SSNs, financial records, and tax filings for hundreds of clients. Every system or tool you build needs to meet industry security standards – encryption in transit and at rest, proper access controls, secure authentication, real monitoring, and adherence to Section 7216\. This is the foundation from which we build.
Outcomes
Here is what we expect this role to deliver.
- Audit and replace – at least 3 high\-cost SaaS tools within the first 12 months with custom\-built alternatives that match or improve on what we have today
- Cut SaaS spend – by at least $40,000 within the first 12 months
- Build and deploy – a minimum of 5 AI\-powered tools or automations within the first 6 months that reduce manual work across the firm
- Keep things running – 99%\+ uptime on all production tools you build, with incidents documented and resolved within 24 hours
- Document everything – All code lives in the shared repo. All systems have written documentation. Nothing critical exists only in your head.
- Deliver a quarterly roadmap – showing what has been completed, what is in progress, and what is planned across all technology priorities.
Attributes
These are the qualities we look for. Not credentials, just how you work.
- Ownership – You build it, you own it. You see things through and take responsibility when something goes sideways.
- Technical Depth – You write real, maintainable code and you understand it at every level. You can debug your own work without needing someone else to step in.
- Resourcefulness – When you hit a wall, you find a way through. You do not wait to be unblocked.
- Business Curiosity – You want to understand how the firm works, not just the technical requirements. The best tools come from people who understand the problem well.
- Communication – You keep people informed. Status updates, blockers, changes in direction – the team always knows where things stand.
- Security Mindset – You think about how data could be exposed or mishandled before anyone asks you to. Client trust is something you take personally.
Non\-Negotiables
- Write real code – You understand what you are shipping. If you cannot explain it, it does not go to production.
- Own what you build – When something breaks, you fix it. We value people with agency who can find solutions to problems they encounter.
- Document as you go – Every system in the shared repo, fully documented. Future teammates should be able to pick up your work without a phone call.
- Protect client data – Our clients share sensitive financial information with us. Every tool you build reflects that responsibility.
- Communicate consistently – As a virtual firm, it’s vital for you to provide consistent updates and ask proactive questions. Keep the team in the loop and you’ll avoid issues before they even surface.
Technical Requirements
This is the first hire of this nature at the firm. We do not have a fully built\-out training program for the specific role and responsibilities themselves, but we will provide thorough training on the software and systems we currently use. We expect you to hit the ground running from a technical standpoint.
*Core Engineering*
- Backend – Python and/or Node.js at a production level
- Frontend – React or equivalent – you can build functional, clean interfaces
- Databases – Solid SQL fundamentals, data modeling, and working knowledge of NoSQL
- APIs – REST, webhooks, OAuth – you have integrated with third\-party services before and know how they work
- Version control – Git, used properly – organized commits, clean history, reviewable code
*AI \& Automation*
- LLM integration – You have built real things with the OpenAI or Claude API, not just exploratory demos
- Agent frameworks – Familiarity with LangChain, AutoGen, or similar tools
- Workflow automation – You have connected AI to real business processes in a way that people actually use
*Security \& Deployment*
- Encryption – You implement encryption in transit and at rest as a default, not an afterthought
- Access controls – Role\-based permissions, MFA, OAuth, and least\-privilege access are standard practice for you
- Compliance awareness – Some familiarity with SOC 2 concepts, data retention requirements, and audit logging in a regulated environment
- Hosting \& deployment – You have shipped and maintained web apps in a real cloud or VPS environment
- Monitoring – You set up logging and alerting proactively so you know about problems before the rest of the team does
*Industry\-Specific Requirements*
We operate in a regulated industry, and that shapes how we build. IRC §7216 governs the use and disclosure of client tax return information, and that includes how AI tools interact with that data. You do not need to know this going in – we will walk you through it. What matters is that you are someone who learns the rules of the environment you are working in and applies them when building. We will provide the compliance context and expect you to build around it.
Metrics of Success
How we will know this is working.
- Weekly (Leading): Project status update delivered by Friday EOD – Every week
- Weekly (Leading): Open incidents or system issues resolved – Within 24 hours
- Monthly (Leading): Active builds and milestones reported – By the 3rd business day of each month
- Quarterly (Lagging): SaaS tools replaced with internal builds – Minimum 1 per quarter in Year 1
- 12\-Month (Lagging): SaaS cost reduction – $40,000 or more
- Ongoing (Leading): Production systems documented in shared repo – 100%
- Ongoing (Leading): Security standards applied to all deployed tools – Zero exceptions
Growth Path
We are hiring with the long term in mind. For the right person, here is where this goes:
- Technology Manager – You own the full roadmap and lead additional engineering resources as the team grows
- Director of Technology – You set the technology strategy for the firm and oversee how AI is integrated across every service line
- Chief Technology Officer – You define and execute our technology vision, strategy, and R\&D efforts – the person driving the innovation agenda that keeps Aiola CPA ahead of the curve
We are building toward this path with intention. The right person will grow into it.
Application Process
1\. Apply. Send your resume and a brief note about the most interesting internal tool or automation you have built. You can include it in your message or email nick@aiolacpa.com directly. Applications without a note will not be considered.
2\. Quick Chat. 15 minutes with our Director of Operations.
3\. Attribute and Aptitude Tests. A few short online assessments, about 10 minutes each.
4\. Team Interview. A conversation with our CEO, Director of Operations, and Senior Tax Advisor.
5\. Case Study. A practical exercise to see how you think and how you build.
6\. 1\-on\-1 Interview. A follow\-up conversation with our CEO.
7\. Offer. If it's a fit, we move quickly.
If this sounds like the right opportunity for you, we would love to hear from you.
*(We respect your privacy – everything stays confidential.)*
Pay: $140,000\.00 \- $165,000\.00 per year
Benefits:
- 401(k) 4% Match
- Dental insurance
- Flexible schedule
- Health insurance
- Life insurance
- Paid holidays
- Paid sick time
- Parental leave
- Unlimited paid time off
- Vision insurance
- Work from home
Work Location: Remote
Salary Context
This $140K-$165K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Aiola CPA, PLLC, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($152K) sits 9% below the category median. Disclosed range: $140K to $165K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Aiola CPA, PLLC AI Hiring
Aiola CPA, PLLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $165K - $165K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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