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About The Role:
CBRE is seeking a highly experienced Senior Change Management Lead to lead and manage the change throughout our Agentic AI HR transformation.
This individual will be responsible for developing and executing the end-to-end change strategy that enables successful adoption of AI-enabled capabilities, new ways of working, and future operating models across the global HR function.
Working closely with executive sponsors, program leadership, regional People teams, Digital & Technology, and external partners, the Senior Change Management Lead will drive stakeholder engagement, communications, readiness, capability building and adoption activities to ensure business outcomes are realized.
This is a highly hands-on role requiring a leader who can operate strategically with senior executives while also personally developing plans, facilitating workshops, creating communications, managing stakeholders and driving execution. The successful candidate will be equally comfortable shaping transformation strategy and delivering the day-to-day activities needed to embed change across the organization. What You'll Do:
- Develop and execute the global change management strategy for the Agentic AI HR transformation.
- Lead stakeholder engagement and influence activities across senior leadership and regional teams.
- Conduct change impact assessments and develop mitigation plans.
- Design and deliver communication, engagement and adoption strategies.
- Establish and manage change champion networks.
- Develop readiness and adoption measurement frameworks.
- Partner with Learning teams to define AI capability-building approaches.
- Facilitate leadership workshops and alignment sessions.
- Create executive presentations, key messages, FAQs and change artefacts.
- Monitor adoption metrics and recommend interventions to improve outcomes.
- Identify and manage change risks and resistance.
- Work closely with program, technology and business teams to integrate change activities into delivery plans.
- Support benefits realization through sustained adoption and behavioral change. 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.
- 10+ years leading organizational change and transformation initiatives within global organizations.
- Experience supporting large-scale technology, digital, AI or business transformation programs.
- Demonstrated ability to influence senior executives and business leaders.
- Strong communication, facilitation and stakeholder management skills.
- Experience developing change, communications, engagement and adoption strategies.
- Ability to operate independently and drive execution in a fast-paced environment.
- Experience within HR transformation, workforce transformation or future-of-work initiatives preferred.
Critical Success Factors
- Strategic thinker with a strong execution mindset.
- Comfortable working with ambiguity and complex stakeholder environments.
- Credible advisor to senior leaders while remaining highly hands-on.
- Strong storytelling, influencing and facilitation skills.
- Data-driven approach to measuring adoption and business outcomes.
- Passionate about AI, innovation and workforce transformation. 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 Senior Change Management Lead position is $135,000 annually and the maximum salary for the Senior Change Management Lead position is $145,000 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 $135K-$145K range is below the median 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 in Demand for This Role
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 ($140K) sits 34% below the category median. Disclosed range: $135K to $145K.
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.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 9,653 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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