Interested in this AI/ML Engineer role at Credit Union of Southern California?
Apply Now →About This Role
Description:
The AVP, Enterprise AI & Automation is responsible for establishing and leading the credit union’s Enterprise AI and Automation function. This role provides strategic direction and hands-on leadership to drive the responsible adoption of artificial intelligence, generative AI, and intelligent automation technologies across the organization.
Reporting directly to the Chief Technology Officer, this role partners closely with IT, business leaders, compliance, risk, and operations to identify high value use cases, deliver measurable efficiency gains, and enhance both Member and team member experiences. The AVP ensures AI initiatives are executed in alignment with CU SoCal’s AI Strategy, Continuous Process Improvement framework, and applicable governance standards. .
This position in based in Anaheim Hills, CA with the opportunity to work a hybrid-remote schedule which consist of 2 days in-office and 3 days working remote from home.
Why Work at CU SoCal?
CU SoCal is one of the fastest growing credit unions and is more than just a place to work. It’s a place where people come before profit, a place where you can build a career that’s more than a job, and where the work you do has meaning and purpose. We offer competitive pay, great health benefits and a culture like no other!
CU SoCal is a Gallup Exceptional Workplace Award Recipient
In 2026, Credit Union of Southern California (CU SoCal) was once again honored with the Gallup Exceptional Workplace Award, marking our third consecutive win and ranking us among the top workplaces worldwide for outstanding employee engagement.
The CU SoCal culture fosters Building Better Lives for our Members, personal development, open communication, and genuine care for each other. We prioritize C.A.R.E. 4: professional competence, positive attitude, relationship building, and eager service to others. If you're passionate about making a difference and want to be part of a dynamic team, please submit your application today!
Requirements: Minimum Qualifications
- Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science or related field.
- 8+ more years of experience in technology leadership, digital transformation, automation or advanced analytics roles.
- Demonstrated experience leading enterprise initiatives involving AI, generative AI, RPA, or intelligent automation.
- Experience defining and executing technology or automation strategies in regulated industries such as financial services, healthcare, or public sector.
- Experience working with vendor provided AI solutions and platforms, including buy and partner deliver models.
- Strong understanding of process improvement methodologies and their application to automation initiatives.
- Experience establishing governance, risk management, and control frameworks for emerging technologies.
- Proficient in all applicable MS Office programs (i.e., Word, Excel, Outlook, Teams, etc.).
Preferred Qualifications
- Master’s degree in Technology, Data Science, AI, Business, or related discipline
Essential Duties and Responsibilities
- Establish and lead the Enterprise AI & Automation function, including defining operating model, intake processes, prioritization frameworks, and delivery standards.
- Develop and maintain the enterprise AI and automation roadmap, ensuring alignment with organizational strategy, business priorities, and technology architecture.
- Identify, evaluate, and prioritize AI, generative AI, and RPA use cases that deliver measurable operational efficiency, scalability, risk reduction, or enhanced Member and team member experiences.
- Partner with business units and IT teams to redesign processes and implement intelligent automation solutions grounded in continuous improvement principles.
- Lead the adoption and optimization of enterprise AI platforms, including currently approved tools such as Microsoft Copilot and ChatGPT, to improve team member productivity, decision making, and operational efficiency.
- Partner with business units as subject matter expert to evaluate, select, and implement AI and automation solutions, providing technical guidance, governance oversight, and vendor coordination while supporting business owned outcomes.
- Ensure all AI initiatives adhere to responsible AI principles, regulatory requirements, and internal governance standards, including alignment with the NIST AI Risk Management Framework and CU SoCal policies.
- Collaborate with compliance, risk management, legal, and data governance stakeholders to support ethical AI usage, data protection, model transparency, and risk mitigation.
- Lead change management and adoption for enterprise-wide AI solutions by partnering with business units and C&TD to support training, communication, and value realization tracking.
- Define and monitor KPIs to measure the effectiveness, adoption, and business impact of AI and automation initiatives.
- Stay current on AI, generative AI, and automation trends, assessing emerging technologies for relevance and potential value to the credit union.
- Build, lead, and develop a high performing team, providing coaching, direction, and performance management.
- Complete understanding and active application the C.A.R.E. 4 Guide expectations and the Brand Communications Standards Accountability Philosophy.
- Must be able and available to work a “hybrid” schedule, per business operations requirement, as needed.
- Additional duties and responsibilities, as assigned.
We support Pay Transparency and are a pay-for-performance organization that benchmarks base pay to the 50th percentile of market data.
Pay Scale: Min. $192,157 / Mid. $245,000 / Max. $315,848* per year
- Actual compensation will be based on geographical location, work experience, education and/or skill level.
CU SoCal is an equal opportunity employer. All decisions are based only on the individual’s qualifications/ability to perform the work. The above statements are intended to describe the essential functions, nature and level or work to be performed as of the document preparation date; they are not intended to be an exhaustive list of all duties and responsibilities. CU SoCal reserves the right to modify this job description at any time, without notice.
We foster an environment and culture where ideas and decisions from all people help us grow, innovate, and fulfill our mission of Building Better Lives.
Salary Context
This $192K-$315K range is above the 75th percentile 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 Credit Union of Southern California, 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 in Demand for This Role
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 ($254K) sits 20% above the category median. Disclosed range: $192K to $315K.
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.
Credit Union of Southern California AI Hiring
Credit Union of Southern California has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Anaheim, CA, US. Compensation range: $315K - $315K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.