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About This Role
About Patelco Credit Union:
Patelco Credit Union is a not-for-profit credit union with a purpose to build financial health and wellbeing for our members. Since 1936, Patelco has grown from $500 in assets to over $9 billion in assets and is the 7th largest credit union in California with branches throughout Northern California.
We are here for our members throughout all their stages of life. Meeting them with the products and services to help them plan purposefully for their futures and to secure our life-long partnership as their trusted financial advocate. As one team, we are all committed to delivering service, empowering financial literacy, creating products, and providing new technology for our members.
We believe that work should be rewarding, challenging, and enjoyable. We’re dedicated to creating a positive and supportive culture where our team members can thrive. If you’re looking to use your skills and knowledge to make a difference in our members’ lives, Patelco could be the perfect fit for you. Overview:
Patelco Credit Union is looking for a Risk Analytics & Data/AI Governance Senior Program Manager to help strengthen how we identify, understand, monitor, and report risk across the organization. This role will use data, analytics, and governance practices to help leaders make informed decisions that support our members, our business, and our long-term risk management goals.
This role will lead analytics-driven risk initiatives, modernize risk reporting capabilities, and help advance Data and AI governance practices across the enterprise. Working closely with cross-functional teams, the Program Manager will help develop reliable risk insights, improve reporting processes, and support strong governance practices across the organization.
The ideal candidate combines strong analytical and technical expertise with experience in credit, financial, or enterprise risk management. You are comfortable translating complex data into clear, actionable insights for executives, governance committees, and business partners. This is a highly collaborative role for someone who enjoys solving complex problems, influencing stakeholders, and driving continuous improvement in risk analytics and governance practices. Responsibilities:
- Lead analytics-driven risk initiatives that improve how financial, credit, operational, and emerging risks are identified, monitored, and reported.
- Build and enhance dashboards, reports, and analytics that help senior leaders and governance committees understand key risk trends and make better decisions.
- Analyze credit portfolio trends, risk concentrations, delinquency, losses, and other indicators to identify emerging risks and business implications.
- Partner with cross-functional teams to define and improve key risk indicators, data definitions, reporting standards, and governance routines.
- Support Data and AI governance activities, including documentation, use case intake, risk assessment, monitoring, and data quality expectations.
- Prepare clear, executive-ready analyses, updates, and recommendations for management, board, audit, regulatory, and governance-related needs.
- Improve reporting automation, consistency, and documentation to reduce manual work and increase confidence in risk information.
Qualifications:
- 7+ years of experience in analytics, credit risk, financial risk, enterprise risk management, data governance, or a related field.
- Experience working in a credit union, banking, fintech, or other regulated financial services environment.
- Strong understanding of consumer credit risk, portfolio analytics, risk indicators, trend analysis, and performance reporting.
- Ability to work with large datasets and use analytical tools such as SQL, Python, R, or similar technologies.
- Experience creating or improving dashboards, automated reports, data models, or analytics processes.
- Experience with business intelligence or visualization tools such as Power BI, Tableau, or similar platforms.
- Strong communication skills, with the ability to explain complex technical, analytical, and risk concepts to senior leaders and business partners.
Target Base Pay:
$120,170 - $151,212 Compensation at Patelco:
Please note that the salary information is a general guideline only. Patelco Credit Union considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/training, key skills, internal peer equity, as well as market and business considerations when extending an offer. We offer a competitive total rewards package including a Short-Term Incentive (STI) program for all regular positions and a wide range of medical, dental, vision, financial, and other benefits. We Offer: Physical Health:* Exceptional Medical, Dental, Vision, and Life Insurance benefits
- Onsite fitness center at HQ and rewards for completing wellness related activities
Financial Health:* Competitive compensation packages with bonus opportunity
- 401(k) with 3% Safe Harbor and 5% employer match
- Discounts on loan products
- Tuition reimbursement
Emotional Health:* Employee Assistance Program (EAP)
- PTO for part-time and full-time positions
- Paid holidays
Personal Development:* On-the-job training and skills development
- Internal transfer opportunities for career growth
- Volunteer work
Flexible work arrangements available for specific positions
Patelco Credit Union is an Equal Opportunity Employer including individuals with disabilities and protected veterans
Salary Context
This $120K-$151K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Patelco Credit Union, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($135K) sits 36% below the category median. Disclosed range: $120K to $151K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Patelco Credit Union AI Hiring
Patelco Credit Union has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dublin, CA, US. Compensation range: $151K - $151K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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