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About This Role
CBRE is using this platform to provide general information to individuals who want to learn more about a career at CBRE. If an opportunity opens that matches your qualifications, we will reach out to you at that time. We are growing a world-class organization with a world-class team and appreciate your interest in CBRE. We welcome you to explore our Careers page and apply directly to any positions that interest you. We will not consider the submission of resumes on this platform as an expression of interest in any posted position at CBRE. About the Role:
As a CBRE Senior AI Engineer, you will be instrumental in designing, building, deploying, and maintaining advanced Artificial Intelligence and Machine Learning (AI/ML) systems that solve complex business challenges and drive innovation. A primary focus of this role will be leveraging Generative AI and Agentic AI frameworks to automate complex workflows and enhance business processes. This role demands a blend of deep technical expertise in AI/ML, strong software engineering principles, and a collaborative spirit to translate intricate data science models into robust, scalable, and production-ready solutions. You will work closely with data scientists, product managers, and other engineering teams, acting as a technical leader and mentor to ensure the successful delivery and operationalization of AI initiatives. Your continuous learning and application of the latest advancements in AI, including Generative AI, will be crucial in identifying strategic opportunities and shaping our AI roadmap. What You’ll Do:
- Lead the end-to-end design, development, and implementation of scalable AI/ML systems and pipelines, emphasizing Generative AI/LLM solutions, including RAG implementation and evaluation.
- Collaborate with data scientists to transition experimental models into robust, production-ready deployments.
- Architect and implement MLOps strategies, covering CI/CD, version control for models and data, automated testing, and infrastructure-as-code for AI solutions.
- Optimize existing AI/ML models for performance, accuracy, scalability, and computational efficiency using advanced techniques.
- Develop and integrate AI/ML services and APIs into existing software applications and platforms for seamless functionality.
- Troubleshoot complex issues across AI/ML model performance, data pipelines, infrastructure, and distributed systems in production.
- Establish and enforce best practices for AI/ML development, deployment, and governance, including data privacy, security, and responsible AI standards.
- Mentor junior AI/ML engineers, providing technical guidance and fostering a culture of continuous learning.
- Conduct research and stay current with advancements in AI, machine learning, deep learning, and Generative AI techniques (e.g., LLMs, Agentic AI, RAG solutions), applying innovative context engineering.
- Contribute expert recommendations to AI roadmap planning and capability development, influencing strategic decisions across multi-discipline teams.
- Communicate complex technical concepts effectively to both technical and non-technical stakeholders. 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 preferred with 8-10 years of relevant experience, or an equivalent combination of education and experience.
- Expert-level proficiency in programming languages like Python, and major AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Demonstrated experience with MLOps practices and tools for model versioning, training, deployment, monitoring, and retraining.
- Strong experience with cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services (e.g., SageMaker, Azure ML, Vertex AI).
- In-depth understanding of machine learning algorithms, deep learning architectures, and their underlying mathematical principles.
- Experience with data processing, feature engineering, and managing large-scale datasets, including proficiency in SQL and big data technologies.
- Proven ability to design, develop, and optimize algorithms for various AI/ML applications, with expert knowledge and practical experience in Generative AI techniques (e.g., LLMs, GANs, VAEs, Agentic AI frameworks), including RAG solution design, implementation, evaluation, and context engineering.
- Advanced problem-solving skills for unique and complex technical challenges with broad business impact.
- Exceptional communication and collaboration skills, with the ability to influence stakeholders at senior levels.
- An innovative and inquisitive mindset to continuously develop new methods and push the boundaries of existing solutions. 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. 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 is using this platform to provide general information to individuals who want to learn more about a career at CBRE. If an opportunity opens that matches your qualifications, we will reach out to you at that time. We are growing a world-class organization with a world-class team and appreciate your interest in CBRE. We welcome you to explore our Careers page and apply directly to any positions that interest you. We will not consider the submission of resumes on this platform as an expression of interest in any posted position at CBRE. 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 9,653 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. Senior-level AI roles across all categories have a median of $228,700.
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
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 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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