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About This Role
Job Description
For more than 80 years, Kaplan has been a trailblazer in education and professional advancement. We are a global company at the intersection of education and technology, focused on collaboration, innovation, and creativity to deliver a best-in-class educational experience and make Kaplan a great place to work.
The future of education is here and we are eager to work alongside those who want to make a positive impact and inspire change in the world around them.
The Senior Manager, GEO Strategy & AI Search Activation provides strategic vision, technical leadership, and off-page activation to maximize visibility across Generative Engine Optimization (GEO) and AI search platforms. Operating across a comprehensive portfolio, this role will support higher education university clients and Kaplan test prep and professional education product lines. The Senior Manager drives innovative strategies designed to position Kaplan brands and partner institutions as primary, trusted sources for Large Language Models (LLMs) and answer engines, including ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews.
The Senior Manager balances hands-on execution and publisher outreach with cross-functional collaboration, guiding internal teams and managing workflows to drive data-driven business impact. This position directly conducts AI visibility and sentiment audits, coordinates multi-tiered publisher outreach pipelines, maintains foundational SEO and content marketing best practices, and collaborates across cross-functional marketing and product teams to expand entity trust, visibility, and citation share across LLM and AI search ecosystems.
Primary/Key Responsibilities
- Execute Channel & GEO Strategy: Formulate, deploy, and manage forward-thinking Generative Engine Optimization (GEO) and off-page search strategies designed to position Higher Education client institutions and Test Prep product lines as preferred, trusted sources across generative AI models.
- Drive Programmatic GEO & Entity Authority: Build programmatic GEO authority across core academic and professional verticals, ensuring institutional entities and brand authority are accurately recognized and reinforced in LLM knowledge bases and Knowledge Graphs.
- Manage Tiered Publisher Engagement & Outreach: Operationalize a multi-tiered publisher engagement model; identify target sites, route high-profile media opportunities to internal or client PR/communications teams, and directly outreach to publications and education portals.
- Audit Sentiment & Correct Third-Party Inaccuracies: Conduct sentiment audits across influential third-party websites, education portals, and industry platforms to identify content inaccuracies, outdated product information, or negative brand drivers; execute targeted publisher outreach to correct data errors and enhance overall brand positioning across search engine results and AI models.
- Analyze and Measure AI Performance Metrics: Monitor, analyze, and report on AI performance metrics including AI visibility scores, sentiment scores, citation share, share-of-voice, and citation placements across ChatGPT, Gemini, Perplexity, Claude, Google AI Mode, and Google AI Overviews using enterprise platforms such as Profound and Ahrefs.
- Integrate SEO & Content Marketing Best Practices: Identify and execute opportunities to incorporate foundational SEO and content marketing best practices (including keyword intent mapping, metadata optimization, internal linking, on-page optimizations, and content freshness) into all GEO initiatives and cross-functional web projects.
- Implement Extractable Content & Information Gain Standards: Partner with internal Content Marketing teams to optimize existing pages and close competitive content gaps with net-new content, structuring owned assets for Retrieval-Augmented Generation (RAG) extraction while embedding proprietary data and Subject Matter Expert (SME) insights to meet Information Gain criteria.
- Mobilize Cross-Functional Alignment: Serve as an authoritative GEO champion across the organization, building partnerships across SEO, Affiliate, Product Marketing, Legal, Creative, Editorial, and Web teams to align messaging and execute integrated AI search strategies.
- Integrate AI into Search Workflows: Strategically utilize AI tools, prompts, and automated workflows (e.g., Claude, Gemini, Python scripts) to accelerate target discovery, sentiment categorization, and performance reporting while maintaining strict standards of human oversight.
Minimum Qualifications
- Bachelor’s Degree with a focus in Marketing, Communications, Information Technology, Computer Science, or a related discipline.
- Minimum of 6 years of experience managing organic search (SEO), digital outreach, or content marketing strategies for multi-website portfolios, including a minimum of 1 year of direct, hands-on experience in Generative Engine Optimization (GEO) and AI search strategy. Demonstrated experience leading cross-functional project workflows and executing off-page outreach campaigns.
- GEO & AI Search Mastery: Deep familiarity with the emerging landscape of generative search, Large Language Model (LLM) retrieval mechanics, Retrieval-Augmented Generation (RAG) concepts, and entity-based SEO frameworks.
- Foundational SEO & Content Strategy: Strong grounding in core organic search methodologies (including keyword research, search intent mapping, metadata, on-page optimizations, content marketing best practices, and internal link architecture) to ensure GEO campaigns align with traditional SEO goals.
- Systems and Analytics Toolsets: Advanced proficiency with enterprise search and AI tracking platforms, including Profound, Ahrefs, SEMrush, Google Search Console, Google Analytics 4, and Looker Studio.
- Strategic Outreach & Publisher Pitching: Proven track record of securing brand mentions, guest contributions, and high-authority citations across industry publications, news outlets, and niche education portals.
- Communication & Problem Solving: Exceptional communication, stakeholder management, and critical thinking skills, with a proven ability to translate complex AI search concepts into actionable business priorities for cross-functional teams.
Preferred Qualifications
- Experience in the higher education or test prep/professional licensure industries.
- Functional understanding of core technical SEO elements (crawling infrastructure, JavaScript rendering, and structured data/JSON-LD schema).
Beyond base salary, our comprehensive total rewards package includes:
- Remote work provides a flexible work/life balance
- Comprehensive Retirement Package automatically enrolled in The Company Contribution Plan (8-10% annual company contribution based on tenure)
- Our Gift of Knowledge Program provides tuition assistance and substantial discounts for our employees and close family members
- Comprehensive health benefits new hire eligibility starts on day 1 of employment
- Generous Paid Time Off includes paid holidays, vacation, personal, sick paid time-off, plus one (1) volunteer day and one (1) diversity and inclusion day to participate and give back to our local communities
We are committed to providing a supportive and rewarding work environment where every employee can thrive. You can learn more about our full benefits package and total rewards philosophy here.
At Kaplan, we believe in attracting, rewarding, and retaining exceptional talent. Our compensation philosophy is designed to be competitive within the market, reflecting the value we place on the skills, experience, and contributions of our employees, while taking into account labor market trends and total rewards.
For full-time positions, Kaplan has three Salary Grades. This position is Salary Grade B: $64,819 to $162,047. The specific compensation offered will be determined by a variety of factors, including but not limited to the candidate's qualifications, relevant experience, education, skills, and market data. We are an equal opportunity employer and comply with all applicable federal and state wage laws.
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Remote/Nationwide, USAAdditional Locations
Employee Type
EmployeeJob Functional Area
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00091 Kaplan Higher EDDiversity & Inclusion Statement:
Kaplan is committed to cultivating an inclusive workplace that values diversity, promotes equity, and integrates inclusivity into all aspects of our operations. We are an equal opportunity employer and all qualified applicants will receive consideration for employment regardless of age, race, creed, color, national origin, ancestry, marital status, sexual orientation, gender identity or expression, disability, veteran status, nationality, or sex. We believe that diversity strengthens our organization, fuels innovation, and improves our ability to serve our students, customers, and communities. Learn more about our culture here.
Kaplan considers qualified applicants for employment even if applicants have an arrest or conviction in their background check records. Kaplan complies with related background check regulations, including but not limited to, the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. There are various positions where certain convictions may disqualify applicants, such as those positions requiring interaction with minors, financial records, or other sensitive and/or confidential information.
Kaplan is a drug-free workplace and complies with applicable laws.
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 Kaplan, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
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.
Kaplan AI Hiring
Kaplan has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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
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