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About This Role
About the Company
We aim become one of thep 100 CPA firms in the US through a culture of continuous improvement, positioning ourselves as the go\-to partner for businesses nationwide and setting the standard for excellence in accounting.
This is an onsite, salaried position located in Irvine, CA.
About the Role
We're not just adopting AI \- we're building it into the DNA of financial services. At GreenGrowth CPAs, we’re looking for an AI Engineer who’s ready lead the charge in transforming how financial operations are automated, predicted, and intelligently scaled. If you thrive in fast\-moving environments, love experimenting with LLMs, and want work somewhere where your ideas will shape the roadmap \- this is your chance. This role comes with a base salary, equity opportunity, and the ability own AI innovation at a company that’s scaling rapidly.
Responsibilities
You’ll own the full lifecycle of intelligent systems from concept deployment focused on automation, predictive analytics, and seamless user experiences.
Build \& Launch AI Solutions:
Design and deploy ML models* automate financial workflows (think: invoicing, forecasting, expense classification).
Develop NLP applications like document parsing, chatbot* ols, and summarization engines.
Embed AI directly into client\-facing and internal* ols alongside product \+ engineering.
Scale the Infrastructure:
- Build secure, scalable AI pipelines for training, deployment, and retraining.
Choose the best\-fit frameworks and cloud* ols (AWS, Azure, GCP).
Monitor model performance and retrain regularly* keep systems sharp.
Collaborate \& Innovate:
Partner with finance, ops, and product teams* identify high\-impact AI use cases.
Experiment with new* ols (LLMs, vector databases, multimodal models—you name it).
Help shape our internal data strategy and make insights visible with dashboards and reporting* ols.
Build Smart \& Safe:
- Ensure all AI systems comply with privacy laws (GDPR, CCPA, etc.).
- Build in transparency and explainability from day one.
- Help us stay ahead of AI/ML trends in fintech and SaaS.
- Maintain and expand our internal platform, a Python/Flask/Postgresql monolith app running on AWS lightsail/Ubuntu Linux.
Qualifications
- Bachelor’s degree in Engineering, Computer Science or a related field.
Required Skills
Proficiency in Python, Postgresql, html/css/Javascript, AWS Devops, Tailwindcss, GoogleScripts, N8N, Zapier, exposure* Quickbooks Intuit API preferred.
Exposure Openai, Anthropic, Groq, or Gemini APIs* create AI wrapper apps.
- Strong experience in automating accounting processes, preferred.
- Proven experience as a full stack developer, Automation or AI Engineer or similar role.
- Strong programming skills in languages such as Python, Ruby, or Javascript.
- Knowledge of software development methodologies and best practices.
- Familiarity with databases, SQL, and scripting.
- Excellent problem\-solving and analytical skills.
Ability* work independently and as a part of a team.
- Excellent spoken and written English.
Who We're Looking For
Someone who wants be early on something big, not just maintain legacy* ols.
Learns fast, builds faster, and isn’t afraid* ask hard questions.
- Gets energy from experimentation and autonomy.
- Wants skin in the game with equity and long\-term impact.
Pay Range and Compensation Package
- Base Salary: $70,000 – $80,000 base compensation
- Health \& Wellness: Comprehensive medical, vision, and dental benefits
- Time Off: Unlimited PTO and observed paid holidays
- Retirement: 3% non\-elective contribution a company 401(k)
The journey doesn't stop here! We have a plethora of benefits unveil during our interview. If you're hungry for growth and thrive in a culture of care and innovation, .
Join GreenGrowth CPAs, where your work truly matters.
GreenGrowth CPAs is an equal opportunity employer that prohibits discrimination and harassment, makes hiring decisions based on job\-related qualifications, provides reasonable accommodations for applicants and employees (including for disabilities and sincerely held religious practices), and complies with federal and California law.
Department
IT
Location
Anaheim, California, USA
Salary Context
This $70K-$80K 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 GreenGrowth CPAs, 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 ($75K) sits 66% below the category median. Disclosed range: $70K to $80K.
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.
GreenGrowth CPAs AI Hiring
GreenGrowth CPAs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Anaheim, CA, US. Compensation range: $80K - $80K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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