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About This Role
The Sr Email Marketing Lead is responsible for leading the execution, operations and strategy for email messaging \- defining and driving the messaging campaign from concept, development to timely deployment of email campaigns across all product lines in Auto Club Enterprises to drive business results. This includes maintaining the email roadmap and content delivery schedules. The role also is responsible for managing the email messaging team on a daily basis and collaborating with multi channels, including but not limited to: Marketing, Product Management, Product Development, and Support teams.
Job Duties
Develop email marketing roadmap and strategies that support business objectives. Drive and achieve business goals including revenue and loyalty metrics through the email marketing channel.
Manage the execution and operational workflow for the email marketing strategy. Manage daily operations of the email marketing channel – marketing calendar, promotional planning, creative design, execution and deployment of email campaigns. Support email strategy with relevant content, segmentation, multi\-channel, multi\-journey communication and personalization using a customer data platform.
Management of the email team that designs develops and deploys email ad hoc and automated campaigns via the Salesforce Marketing Cloud platform.
Manage all Project Tracker tasks, updates, assignments and campaign information.
Lead / monitor quality control efforts on all email campaigns.
Manage brand and email identity standards.
Assist with cloud data process creation and execution. Manage Salesforce Administration (e.g. setup, user management, support case management).
Oversee email campaign reporting to business lines.
Review the competitive landscape and implement best practices for email marketing.
Maintain a solid understanding of breadth of member products, services, and benefits.
Hire, onboard and train new team members.
The Senior Lead Email Marketng Producer acts as a complex problem solver and project manager for email marketing, understanding all aspects of the email build from the building of assets, creative and responsive design, segmentation and deployment organization.
Qualifications
Bachelors Business Administration/Management Preferred
Bachelors Marketing Preferred
Bachelors Business Communications Preferred
7\-9 years Email marketing including experience with Major Email Service Providers such as Salesforce Marketing Cloud Required
1\-3 years Supervisory experience managing a high performaning team Required
Experience using quantitative and qualitative data to identify product opportunities, inform product vision and strategy.
Experience with Insurance/Travel/Discount/Automotive/Membership products and operations highly desired.
Advanced ability to think strategically and transform strategy into actionable plans.
Clear understanding of UI/UX design and the basic principles of usability.
Extensive knowledge of the Salesforce Marketing Cloud platform.
Knowledge of project management tools (e.g., Monday.com, Jira)
Knowledge of digital content, best practices, and emerging trends.
Excellent communication and interpersonal skills for working in a collaborative environment.
Strong organizational skills.
Keen attention to detail.
Excellent time management and capability to manage multiple projects simultaneously.
Flexibility to make changes and quickly adapt to changing business needs.
Willingness to innovate and challenge the status\-quo.
The starting pay range for this position is:
$120,500\.00 \- $160,800\.00
Additionally, for full time positions, you will be eligible to participate in our incentive program based upon the achievement of organization, team and personal performance.
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Remarkable benefits:
- Health coverage for medical, dental, vision
- 401(K) saving plans with company match AND Pension
- Tuition assistance
- Floating holidays and PTO for community volunteer programs
- Paid parental leave
- Wellness programs
- Employee discounts (membership, insurance,
travel, entertainment, services and more!)
Auto Club Enterprises is the largest club within the national AAA federation. We have nearly 17,000 employees in 24 states helping more than 18 million members. The strength of our organization is our employees. Bringing together and supporting different cultures, backgrounds, personalities, and strengths creates a team capable of delivering legendary, lifetime service to our members. When we embrace our diversity – we win. All of Us! With our national brand recognition, long\-standing reputation since 1900, and constantly growing membership, we are seeking career\-minded, service\-driven professionals to join our team.
“Through dedicated employees we proudly deliver legendary service and beneficial products that provide members peace of mind and value.”
AAA is an Equal Opportunity Employer
Our organization participates in E\-Verify
The Automobile Club of Southern California will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable federal, state, and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance (FCIHO), the Unincorporated Los Angeles County (ULAC) regulation, and the California Fair Chance Act (CFCA).
Salary Context
This $120K-$160K 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 Auto Club of Southern Calif, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 16% below the category median. Disclosed range: $120K to $160K.
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.
Auto Club of Southern Calif AI Hiring
Auto Club of Southern Calif has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Alhambra, CA, US, Costa Mesa, CA, US, Del Mar, CA, US. Compensation range: $47K - $160K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 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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