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About This Role
CBIZ, Inc. (NYSE: CBZ) is a leading professional services advisor to middle\-market businesses nationwide. With industry knowledge and expertise in accounting, tax, advisory, benefits, insurance, and technology, CBIZ delivers actionable insights to help clients anticipate what is next and discover new ways to accelerate growth. CBIZ has more than 9,500 team members across 23 major markets coast to coast.
CBIZ strives to be our team members' employer of choice by creating an environment where team members are appreciated, recognized for their contributions, and provided with opportunities to grow, both personally and professionally, throughout their careers.
Together, CBIZ and CBIZ CPAs are ranked as one of the top providers of accounting services in the United States. CBIZ CPAs is an independent CPA firm that provides audit, review and attest services, while CBIZ provides business consulting, tax and financial services. In certain jurisdictions, CBIZ CPAs operates under its previous name, Mayer Hoffman McCann P.C.
Minimum Qualifications
- Bachelor's degree
- 7 years experience in public accounting or related field
- 5 years supervisory
- Must have and preserve required licenses
- Ability to manage deadlines, work on multiple assignments and prioritize each assignment as necessary
- Demonstrated ability to communicate verbally and in writing throughout all levels of organization, both internally and externally
- Proficient use of applicable technology
- Must be able to travel based on business needs
This role sits at the center of CBIZ Technology’s AI Advisory \& Transformation growth strategy, supporting one of the firm’s most critical and high\-visibility practices.
The Senior Marketing Manager is responsible for leading the strategic marketing direction and execution for AI Advisory \& Transformation services, driving market positioning, demand generation, and pipeline growth. This role partners closely with practice leadership while staying aligned with the national marketing team to ensure consistency across brand, messaging, and campaign execution.
This individual combines strategic thinking with strong execution leadership, overseeing integrated marketing programs and coordinating internal and external execution partners. The role requires a deep understanding of modern marketing trends, including AI\-influenced search, evolving buyer behavior, and emerging digital channels, as well as firsthand exposure to AI transformation initiatives.
Essential Functions and Primary Duties
Strategic Leadership \& Practice Alignment
- Define and lead the marketing strategy for the AI Advisory \& Transformation practice, identifying and targeting key audience segments, aligned to revenue goals, priority industries, and solutions
- Partner with practice leaders and sales teams to shape go\-to\-market strategy, positioning, and messaging
- Ensure alignment with national marketing strategy, enterprise campaigns, and brand standards
Identify market opportunities, buyer trends, and competitive insights to inform marketing direction
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Integrated Campaign Strategy \& Execution
- Lead the design and execution of integrated marketing programs across digital, content, events, email, and partner channels
- Build structured, full\-funnel campaigns that support awareness, engagement, lead generation and conversion
- Align marketing efforts with sales and SDR teams to drive measurable pipeline contribution
Oversee execution through a mix of internal resources and external execution partners, ensuring quality, speed, and consistency
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Digital, Search \& Emerging Marketing Capabilities
- Own digital strategy across SEO/AEO, paid media, web, and content performance
- Use predictive client insights to scale omnichannel paid advertising and lead generation programs while identifying ways to lower acquisition costs
- Adapt marketing programs to evolving search behavior, including AI\-driven search experiences and content discovery models
- Stay ahead of emerging marketing trends, tools, and channels to continuously improve performance and differentiation
Ensure strong alignment between content strategy, website experience, and conversion pathways
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AI Integration \& Innovation
- Integrate AI into marketing strategies, programs, and workflows to improve performance and efficiency
- Help position CBIZ as a leader in AI\-driven advisory and transformation services through differentiated messaging and thought leadership
Support the internal integration and adoption of AI tools across operations, learning and development, and client\-facing teams.
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Content \& Messaging Leadership
- Drive development of compelling, business\-focused messaging that translates complex AI and transformation services into client value
- Oversee creation of campaign assets, thought leadership, case studies, and sales enablement materials
- Partner with SMEs and practice leaders to ensure technical accuracy and relevance
Identify thought leadership opportunities for key business leaders, building trust and establishing deep industry authority, to drive business growth
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Team Leadership \& Execution Oversight
- Lead and mentor a team of marketing professionals
- Establish clear priorities, workflows, and accountability across team members and execution partners
- Use project management tools (e.g., Asana) to manage timelines, dependencies, and cross\-functional coordination
Ensure delivery of high\-quality campaigns on time and within scope
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Performance Measurement \& Optimization
- Track and report on campaign performance, pipeline contribution, and ROI
- Use data and insights to continuously refine strategy, messaging, and channel mix
Monitor market dynamics and adjust plans to maximize impact and efficiency
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Preferred Qualifications
- Bachelor’s degree in Marketing, Business, Communications, or related field (MBA preferred)
- 10\+ years of experience in B2B marketing, preferably in consulting, digital agency, professional services, or technology
- Experience supporting AI, digital transformation, or advisory service offerings
- Direct experience working within an organization that has undergone AI transformation, or supporting clients through AI transformation consulting and execution
- Strong strategic marketing experience with demonstrated ownership of go\-to\-market planning and , campaign strategy and channel execution
- Proven ability to lead integrated, omnichannel marketing programs that drive measurable pipeline results
- Experience managing both internal teams and external execution partners across digital, content, and campaign delivery
- Demonstrated leadership experience managing direct reports and driving execution through a team
- Hands\-on experience with SEO/AEO, paid media, and digital performance optimization
- Familiarity with AI\-driven marketing tools, AI\-influenced search trends, AI workflows, and emerging digital channels
- Proficiency with project management tools such as Asana (or similar platforms); Hubspot, Pardot or other marketing automation platforms
- Strong ability to translate complex technical concepts into clear, executive\-level messaging
- Ability to operate effectively in a fast\-paced, matrixed environment with multiple stakeholders
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CBIZ, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
CBIZ AI Hiring
CBIZ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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