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About This Role
About Us:Simon Wiesenthal Center (SWC) is a global Jewish human rights organization that confronts antisemitism and hate, defends the safety of Israel and Jews worldwide, and teaches the lessons of the Holocaust through awareness, advocacy, justice, education, and its Museums of Tolerance. With a constituency of over 400,000 member families since its founding in 1977, the SWC has an international footprint through its Museums of Tolerance, Moriah Films
Headquartered in Los Angeles, it maintains operations in New York, Chicago, Miami, Toronto, Paris, Berlin, Jerusalem, and Buenos Aires. It is an accredited Non\-Governmental Organization (NGO) at the United Nations, UNESCO, the Organization of American States, the Latin American Parliament, and the Council of Europe and maintains relationships with the highest levels of governments throughout the world.
In 1993, the Center opened the Museum of Tolerance in Los Angeles to worldwide acclaim. The Museum has served as the Center’s flagship educational arm, challenging visitors to confront bigotry and racism, and to understand the Holocaust in both historical and contemporary contexts. In addition, the Center developed Moriah Media to produce theatrical documentaries to educate global audiences. Moriah has produced 17 documentaries to date, two of which have received the Academy Award for best feature documentary, The Long Way Home and Genocide.
About the Role:The Communications \& Marketing Specialist plays a central role in driving audience growth and engagement for the Museum of Tolerance (MOT) and the Mobile Museum of Tolerance (MMOT), with a strong focus on email marketing strategy, subscriber growth, and digital engagement.
This role leads the planning, execution, and optimization of email campaigns and digital communications that expand reach, deepen audience relationships, and increase participation in educational programs. The ideal candidate is both creative and analytical—skilled in storytelling, segmentation, and performance tracking—and passionate about advancing human rights education through impactful, data\-informed marketing.
Key ResponsibilitiesEmail Marketing \& Audience Growth (Primary Focus)
- Develop and execute a comprehensive email marketing strategy to grow, engage, and retain MOT/MMOT audiences.
- Build and manage segmented email lists (educators, donors, students, community partners, etc.) to deliver targeted messaging.
- Create and optimize email campaigns, including newsletters, program promotions, event invitations, and automated journeys.
- Conduct A/B testing on subject lines, content, and send times to improve open rates, click\-through rates, and conversions.
- Monitor and report on key performance metrics (growth, engagement, conversions) and continuously refine strategy based on insights.
- Partner with internal teams to align email campaigns with organizational goals, programs, and fundraising initiatives.
Digital Marketing \& Content Strategy
- Develop and execute a comprehensive social media content and marketing strategy to grow the Museum’s presence and engagement across platforms such as Instagram, Facebook, and others.
- Develop integrated digital campaigns that support email growth, including lead generation and audience acquisition strategies.
- Create compelling, audience\-focused content across email, web, and social platforms.
- Collaborate with digital teams to optimize landing pages and conversion pathways for campaigns.
- Ensure consistency in voice, branding, and messaging across all digital touchpoints.
Marketing \& Communications
- Support the development and execution of marketing strategies to promote MOT/MMOT programs, exhibits, and initiatives.
- Produce high\-quality content for print collateral, web, and promotional materials.
- Contribute to donor communications, annual reports, and institutional storytelling efforts.
Social Media \& Engagement
- Manage and grow MOT/MMOT social media channels (Instagram, Facebook, X/Twitter, LinkedIn, YouTube) with a focus on driving email sign\-ups and audience engagement.
- Track social and digital engagement metrics and identify opportunities for growth and optimization.
Event Promotion \& Outreach
- Lead promotional campaigns for museum events, exhibit launches, MMOT site visits, and public programming, with an emphasis on digital and email\-driven attendance.
- Collaborate with schools, community organizations, other cultural museums and partners to expand reach and participation.
- Develop marketing toolkits to support partners in promoting programs.
Press \& Media Support
- Collaborate with the in\-house PR team to develop and execute strategies that market and promote upcoming Museum events and initiatives.
- Draft press releases and media materials to support key campaigns and announcements.
- Assist with media outreach efforts and track, report, and analyze earned media coverage.
Qualifications
- Bachelor’s degree in Communications, Marketing, Public Relations, Journalism, or a related field.
- 2–4 years of experience in email marketing, digital marketing, or growth\-focused communications, preferably in a nonprofit or educational setting.
- Demonstrated experience with email marketing platforms (e.g., Mailchimp, HubSpot, Salesforce Marketing Cloud, or similar).
- Strong understanding of audience segmentation, lifecycle marketing, and performance analytics.
- Excellent writing, editing, and storytelling skills tailored for digital audiences.
- Experience with social media management and digital marketing tools (e.g., Hootsuite, Buffer).
- Familiarity with design tools such as Canva or Adobe Creative Suite.
- Highly organized, detail\-oriented, and comfortable managing multiple campaigns simultaneously.
- Passion for social justice, education, and community impact.
Our Benefits:
We value our people and offer a collaborative and engaging culture. As a SWC employee, you will enjoy work/life balance, generous time off and comprehensive benefits and programs. The Simon Wiesenthal Center embraces inclusivity and values our diverse community. We are committed to building a team based on qualifications, merit, and business need. We are proud to be an equal opportunity employer and do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Salary Context
This $56K-$60K range is below 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 Simon Wiesenthal Center, 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 ($58K) sits 65% below the category median. Disclosed range: $56K to $60K.
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.
Simon Wiesenthal Center AI Hiring
Simon Wiesenthal Center has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $60K - $60K.
Location Context
AI roles in Los Angeles pay a median of $178,000 across 1,695 tracked positions. That's 3% below the national 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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