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About This Role
About The Role:
We're looking for a hands-on Manager, AI Visibility & Content Discoverability to own how CBRE is found, trusted, and surfaced accurately across generative AI platforms and conversational search. This role centers on AI-driven discoverability, built on the traditional SEO foundations, clean architecture, structured data, authoritative content, that both search engines and AI systems rely on.
You will work closely with Digital Marketing, Digital & Technology (D&T), and business segment teams across a global, matrixed organization, translating strategy into measurable outcomes and making complex search performance data accessible and actionable for stakeholders at every level. This is an individual contributor role with high ownership. You will bring initiatives to life, improve them over time, and exercise good judgment about what is working and what could be better, spotting opportunities and acting on them. What You'll Do: AI Search and Generative Engine Optimization
- Win CBRE citations and answer inclusion across AI platforms like ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews, growing how often we surface as the trusted source when buyers ask AI about commercial real estate.
- Track where CBRE appears across these platforms, monitor citation share against our competitive set, find the gaps, and act on them with content and production teams.
- Execute CBRE's playbooks for AI-powered search, so our brand is represented accurately and authoritatively.
- Stay ahead of developments in Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and AI-driven discovery, bringing forward specific, grounded recommendations as the space evolves. Automation and Tooling
- Identify where AI and automation can take over the manual layer of search work, monitoring, content optimization, metadata enrichment, and reporting, and translate those needs into clear requirements for our Martech, D&T, and agency partner to build.
- Run these workflows day to day, turning what they surface into faster fixes and optimizations.
- Get full value from CBRE's AI visibility and SEO tool stack, and bring a clear point of view on where automation should extend it next.
Technical Foundation
- Keep CBRE's US properties technically sound so both search engines and AI systems can find, read, and cite them, covering crawlability, schema markup, structured data, Core Web Vitals, and hreflang, in direct partnership with D&T.
- Run regular audits across site architecture, indexation, and page speed. Work with our agency partner to turn findings into clear engineering briefs and see fixes through to completion.
- Grow measurable organic visibility through keyword strategy, on-page optimization, and content gap analysis that reinforce the topical authority AI models recognize. Measurement and Insight
- Own a single view of CBRE's visibility across AI and traditional search, citation share, share of voice, and organic performance, and use it to show what is working and where to move next.
- Design experiments that lift AI citations and organic performance, and recommend new metrics as search behavior shifts.
- Translate performance data into clear recommendations that guide content, technical, and business decisions. Collaboration, Frameworks, and Enablement
- Set the standards and frameworks that keep CBRE's AI presence coherent as regional teams come online, so activity across markets reinforces one brand rather than pulling in different directions.
- Contribute to internal projects including site migrations, content audits, new market launches, and platform rollouts. What You'll Need:
To perform this job successfully, an individual will need to perform each crucial duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions.
- Bachelor's degree in marketing, communications, digital media, or a related field, or equivalent professional experience.
- 5 to 7 years of experience in SEO or search strategy, with a proven record on large-scale enterprise websites.
- Hands-on experience with AI-powered discovery platforms, large language models (LLMs), or answer engines, and a clear point of view on how AI is changing search.
- Fluent enough with AI and automation to see where they replace manual work and to spec what's needed, working with technical partners to build it
- Solid technical SEO skills across structured data, crawl management, site architecture, and performance optimization.
- Sharp analytical instincts. You can read data, form a view, and turn it into a recommendation that drives action.
- Proficiency with SEO platforms such as Semrush, Ahrefs, or Moz, and analytics tools like Google Analytics (GA4) and Google Search Console. Familiarity with AI-visibility tools such as Profound or Scrunch and technical site-health crawlers like Lumar or similar tools is a plus.
- Experience in a global, matrixed organization, building relationships across functions and regions to get things done.
- The ability to turn complex search data into a clear story for any audience, whether an engineering team, a content team, or leadership.
- A natural sense of ownership. You take projects through to completion and raise your hand when something could be better.
- Working knowledge of HTML and CMS platforms. Sitecore experience is a plus. Why This Role Matters:
AI is now part of almost every search experience, and the brands that show up well are the ones investing now. This role is how CBRE stays ahead of that shift, making sure our research, insights, and services are found, cited, and trusted wherever our audiences search. Why CBRE
When you join CBRE, you become part of the global leader in commercial real estate services and investment that helps businesses and people thrive. We are dynamic problem solvers and forward-thinking professionals who create significant impact. Our collaborative culture is built on our shared values — respect, integrity, service and excellence — and we value the diverse perspectives, backgrounds and skillsets of our people. At CBRE, you have the opportunity to chart your own course and realize your potential. We welcome all applicants. Our Values in Hiring
At CBRE, we are committed to fostering a culture where everyone feels they belong. We value diverse perspectives and experiences, and we welcome all applications. Disclaimers
Applicants must be currently authorized to work in the United States without the need for visa sponsorship now or in the future. Applicant AI Use Disclosure
We value human interaction to understand each candidate's unique experience, skills and aspirations. We do not use artificial intelligence (AI) tools to make hiring decisions, and we ask that candidates disclose any use of AI in the application and interview process. About CBRE Group, Inc.
CBRE Group, Inc. (NYSE:CBRE), a Fortune 500 and S&P 500 company headquartered in Dallas, is the world’s largest commercial real estate services and investment firm (based on 2024 revenue). The company has more than 140,000 employees (including Turner & Townsend employees) serving clients in more than 100 countries. CBRE serves clients through four business segments: Advisory (leasing, sales, debt origination, mortgage serving, valuations); Building Operations & Experience (facilities management, property management, flex space & experience); Project Management (program management, project management, cost consulting); Real Estate Investments (investment management, development). Please visit our website at www.cbre.com.
CBRE carefully considers multiple factors to determine compensation, including a candidate’s education, training, and experience. The minimum salary for the Manager, AI Visibility & Content Discoverability position is $100,000 annually and the maximum salary for the Manager, AI Visibility & Content Discoverability position is $115,000 annually. The compensation offered to a successful candidate will depend on their skills, qualifications, and experience. Successful candidates will also be eligible for a discretionary bonus based on CBRE’s applicable benefit program. This role will provide the following benefits: 401(K), Dental insurance, Health insurance, Life insurance, and Vision insurance.
Equal Employment Opportunity: CBRE has a long-standing commitment to providing equal employment opportunity to all qualified applicants regardless of race, color, religion, national origin, sex, sexual orientation, gender identity, pregnancy, age, citizenship, marital status, disability, veteran status, political belief, or any other basis protected by applicable law.
Candidate Accommodations: CBRE values the differences of all current and prospective employees and recognizes how every employee contributes to our company’s success. CBRE provides reasonable accommodations in job application procedures for individuals with disabilities. If you require assistance due to a disability in the application or recruitment process, please submit a request via email at [email protected] or via telephone at +1 866 225 3099 (U.S.) and +1 866 388 4346 (Canada).
Salary Context
This $100K-$115K 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 CBRE, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($107K) sits 49% below the category median. Disclosed range: $100K to $115K.
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.
CBRE AI Hiring
CBRE has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Richardson, TX, Remote, US, New York, NY, US. Compensation range: $115K - $255K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above 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 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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