Interested in this AI/ML Engineer role at CBRE?
Apply Now →Skills & Technologies
About This Role
Position Summary
The Senior Manager, AI, Technology, and Innovation Solutions is a pivotal leadership role within the Center of Excellence (CoE) for Facilities Management & Engineering. Reporting directly to the Global Director of Facilities Management & Engineering, this position is responsible for driving the research, piloting, and implementation of innovative technology solutions—including artificial intelligence (AI), digital twins, advanced analytics, and customer workplace facing applications—to optimize data governance, facilities operations, sustainability, business outcomes, and workplace experience. The Senior Manager will oversee the full lifecycle of technology innovation: from scouting and evaluating emerging solutions, to managing pilots and scaling successful initiatives, to ensuring robust data collection, analysis, governance, and stakeholder reporting. Additionally, the role includes direct management and development of two Technology Analysts, fostering a high-performance team culture aligned with the CoE’s mission of operational excellence and continuous improvement.
This position requires a unique blend of technical expertise in AI and digital technologies, strategic vision, project management acumen, and strong leadership and communication skills. The Senior Manager will serve as a key interface between technical teams, business stakeholders, and external partners, ensuring that technology investments are aligned with organizational goals, regulatory requirements, and industry best practices. The ideal candidate will demonstrate a proven track record in leading technological innovation within complex, global environments, particularly in facilities, operations, or related domains.
________
Key Responsibilities
- Technology Scouting and Solution Evaluation
- Lead the identification, research, and evaluation of innovative technology solutions (AI applications, IoT, digital twins, predictive analytics, energy management platforms, workplace enhancement, etc.) relevant to facilities management, engineering, workplace experience, and sustainability objectives.
- Conduct comprehensive technology scouting by leveraging patent and literature reviews, market analysis, and benchmarking against industry leaders and peer organizations.
- Assess technical, commercial, legal, environmental, and regulatory suitability of candidate solutions, including risk, scalability, sustainability, and compliance with global standards.
- Develop business cases and recommendations for technology adoption, including cost-benefit analysis, ROI projections, and alignment with strategic priorities.
- Pilot Program Management and Implementation
- Design, plan, and execute pilot programs for selected technology solutions, ensuring clear objectives, success metrics, and stakeholder alignment from inception.
- Oversee pilot instrumentation, data collection, and monitoring, ensuring data integrity, privacy, and security throughout the pilot lifecycle.
- Iterate and refine pilot approaches based on real-time feedback, data analysis, and stakeholder input, employing agile methodologies and continuous improvement principles.
- Manage cross-functional teams and external partners during pilot execution, ensuring roles, responsibilities, and deliverables are clearly defined and met.
- Data Collection, Instrumentation, and Analysis Oversight
- Establish and oversee robust data collection frameworks for pilots and implementations, ensuring data quality, consistency, and compliance with privacy and security standards.
- Lead the analysis of pilot and operational data to extract actionable insights, validate hypotheses, and inform decision-making.
- Develop and maintain reporting dashboards that provide real-time visibility into key performance indicators (KPIs), pilot outcomes, and business impact.
- Ensure data governance, ethical use, and regulatory compliance in all data-related activities, including adherence to GDPR, CCPA, and other relevant frameworks.
- Stakeholder Engagement and Pilot Results Delivery
- Synthesize and deliver pilot results, insights, and recommendations to a diverse set of stakeholders, including executive leadership, operations teams, and external partners.
- Develop and present clear, actionable reports and presentations that translate technical findings into business value and strategic guidance.
- Facilitate stakeholder workshops, feedback sessions, and decision-making forums to ensure alignment, buy-in, and informed scaling decisions.
- Champion a culture of transparency, learning, and continuous improvement across all technology innovation initiatives.
- Team Leadership and Analyst Development
- Directly manage, mentor, and develop two Technology Analysts, providing guidance, performance feedback, and professional growth opportunities.
- Foster a collaborative, high-performance team culture that encourages innovation, accountability, and continuous learning.
- Delegate tasks and projects effectively, ensuring clear expectations, resource allocation, and timely delivery of results.
- Support the recruitment, onboarding, and training of new team members as the team scales.
6. CoE Integration, Governance, and Reporting Structure
- Align all technology innovation activities with the Center of Excellence’s (CoE) governance frameworks, standards, and strategic objectives.
- Collaborate closely with the Global Director of Facilities Management & Engineering to ensure reporting, escalation, and decision-making processes are clear and effective.
- Contribute to the development and enforcement of CoE policies related to technology adoption, data governance, risk management, and sustainability.
- Represent the CoE in cross-functional and external forums, sharing best practices and thought leadership.
7. Vendor Selection, Procurement, and Contract Management
- Lead the evaluation, selection, and management of technology vendors and solution providers, in partnership with CBRE Procurement & Client representatives, ensuring alignment with business needs, technical requirements, and compliance standards.
- Negotiate contracts, service level agreements (SLAs), and partnership terms to maximize value and mitigate risk.
- Monitor vendor performance and manage relationships to ensure successful delivery and ongoing support.
8. Budgeting, Business Case Development, and Financial Oversight
- Develop and manage budgets for technology pilots and implementations, ensuring alignment with strategic priorities and financial constraints.
- Prepare detailed business cases and ROI analyses to support investment decisions and resource allocation.
- Monitor and report on financial performance, identifying opportunities for cost optimization and value creation.
9. Risk Management, Compliance, and Regulatory Oversight
- Identify, assess, and mitigate risks associated with technology pilots and implementations, including technical, operational, legal, and reputational risks.
- Ensure compliance with relevant regulations and standards (e.g., GDPR, CCPA, ISO, NIST, industry-specific requirements).
- Develop and enforce policies and procedures for ethical AI use, data privacy, security, and bias mitigation.
- Conduct regular audits and reviews to ensure ongoing compliance and risk management.
10. Organizational Change Management and Adoption
- Develop and execute change management strategies to drive adoption of new technologies and processes across Workplace Operations teams.
- Design and deliver training, onboarding, and communication programs to support successful implementation and user engagement.
- Monitor adoption metrics and user feedback, iterating approaches as needed to maximize impact and minimize resistance.
11. Workplace Operations Use Cases
- Champion the use of AI and digital technologies to drive operational excellence in all workplace operations functions, in partnership with Workplace Operations, Sustainability, Occupancy, Security, Safety, Food, and Project Management Teams.
- Identify and implement use cases such as AI technologies, digital twins, energy optimization, customer experiential technologies, and smart building adaptation.
- Monitor and report on
Required Qualifications
- Education:
- + Bachelor’s degree in Computer Science, Engineering, Information Technology, Facilities Management, or a related field. + Advanced degree (Master’s or PhD) in a relevant discipline preferred.
- Experience:
- + Minimum 8 years of progressive experience in technology innovation, AI, data analytics, or digital transformation, with at least 3 years in a managerial or leadership role. + Proven track record of leading technology & innovation pilots, implementations, and cross-functional teams in complex, global environments. + Experience in facilities management, operations, engineering, or related industries highly desirable.
- Certifications:
- + Relevant professional certifications such as PMP, ITIL, AWS/Azure/Google Cloud certifications, Certified AI Professional, or equivalent. + Data privacy, security, or risk management certifications (e.g., CISSP, CISA, CRISC) are a plus.
Preferred Experience
- Industry Experience:
- + Background in facilities management, engineering, manufacturing, energy, healthcare, or other asset-intensive industries. + Experience with sustainability, energy management, or smart building technologies.
- Technical Experience:
- + Hands-on experience with AI/ML platforms (e.g., TensorFlow, PyTorch, Azure AI, AWS SageMaker, Google Vertex AI). + Familiarity with digital twin platforms, IoT, MLOps, and cloud infrastructure. + Experience with data governance, privacy, and security frameworks.
- Operational Experience:
- + Leading pilots and scaling technology solutions from proof-of-concept to enterprise-wide deployment. + Managing vendor relationships, procurement, and contract negotiations. + Developing and managing budgets, business cases, and financial models.
- Change Management:
- + Designing and executing change management, training, and adoption programs. + Experience in organizational transformation and stakeholder engagement.
Skills and Competencies
Technical Skills
- AI and Machine Learning:
- + Deep understanding of AI/ML concepts, algorithms, and frameworks. + Experience with model development, deployment, monitoring, and MLOps best practices. + Familiarity with generative and agentic AI, natural language processing, computer vision, and predictive analytics.
- Data Management:
- + Expertise in data collection, instrumentation, analysis, and visualization. + Strong knowledge of data governance, privacy, security, and compliance requirements. + Experience with data platforms, ETL pipelines, and dashboarding tools.
- Digital Twins and IoT:
- + Understanding of digital twin concepts, platforms, and integration with IoT sensors and building management systems. + Experience with simulation, predictive modeling, and real-time monitoring.
- Cloud and Infrastructure:
- + Proficiency in cloud platforms (AWS, Azure, Google Cloud) and related services. + Experience with infrastructure automation, CI/CD pipelines, and DevOps/MLOps practices.
Leadership and Management Skills
- Strategic Vision:
- + Ability to align technology innovation with business goals and strategic priorities. + Experience in developing and executing multi-year technology roadmaps.
- Project and Program Management:
- + Expertise in managing complex projects, pilots, and programs from inception to completion. + Strong organizational, planning, and resource management skills.
- Team Leadership:
- + Proven ability to lead, mentor, and develop high-performing teams. + Experience in managing direct reports, delegating tasks, and fostering a collaborative culture.
- Stakeholder Engagement:
- + Exceptional communication and interpersonal skills, with the ability to influence and align diverse stakeholders. + Experience in delivering executive presentations, facilitating workshops, and managing cross-functional teams.
Business and Financial Acumen
- Business Case Development:
- + Ability to develop robust business cases, ROI analyses, and investment proposals. + Experience in budgeting, financial oversight, and cost optimization.
- Vendor and Contract Management:
- + Skills in evaluating, selecting, and managing technology vendors and partners. + Experience in negotiating contracts, SLAs, and partnership agreements.
- Risk Management:
- + Expertise in identifying, assessing, and mitigating risks related to technology, data, and operations. + Knowledge of compliance, regulatory, and ethical frameworks.
Change Management and Adoption
- Change Leadership:
- + Ability to design and execute change management strategies that drive adoption and minimize resistance. + Experience in developing training, onboarding, and communication programs.
- Continuous Improvement:
- + Commitment to learning, feedback, and iterative improvement. + Ability to adapt strategies based on data, feedback, and evolving business needs.
Legal, Ethical, and Sustainability Competencies
- Ethical AI and Bias Mitigation:
- + Knowledge of ethical AI principles, bias mitigation strategies, and responsible AI frameworks. + Experience in developing and enforcing policies for ethical AI use.
- Sustainability and Energy Management:
- + Understanding of sustainability metrics, energy management strategies, and environmental compliance. + Experience in implementing AI and digital solutions to drive sustainability outcomes.
Additional Competencies
- Innovation and Creativity:
- + Ability to think creatively, challenge the status quo, and drive continuous innovation.
- Learning Agility:
- + Commitment to ongoing learning and professional development.
- Diversity and Inclusion:
- + Commitment to fostering an inclusive, diverse, and equitable team environment.
CBRE carefully considers multiple factors to determine compensation, including a candidate’s education, training, and experience. The minimum salary for the position is $175,000.00 annually and the maximum salary for the position is $200,00.00 annually. The compensation that is offered to a successful candidate will depend on the candidate’s skills, qualifications, and experience. Successful candidates will also be eligible for a discretionary bonus based on CBRE’s applicable benefit program.* Benefits for Full-Time Employees
- Corporate welfare benefits, which includes medical, dental, vision, disability, health care and dependent care reimbursement accounts, life and AD&D insurance
- 401(k) Plan
- Paid time off, parental leave, and holidays are available as established by Company policy 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).
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 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 $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.
CBRE AI Hiring
CBRE has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, San Jose, CA, US, Richardson, TX, US. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.