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About This Role
Description
The Manager, Performance Strategy \& AI Activation is responsible for evolving how CDW defines, measures, and accelerates individual and organizational performance. This role shapes the enterprise performance strategy and helps ensure performance practices support business priorities, reinforce the CDW Way, and enable an AI\-first, outcome\-obsessed mindset.
This role connects performance strategy, year\-round prioritization, manager and coworker conversations, technology enablement, and AI adoption into a practical experience that helps leaders and coworkers focus on the work that matters most. It also leads enterprise AI activation efforts, helping translate AI fluency strategy into practical adoption through champion networks, enterprise campaigns, and workforce enablement initiatives. Working across Coworker Success (HR) and with business leaders, this individual brings an enterprise mindset to designing solutions that strengthen performance, drive behavior change, and prepare the workforce for the future. The position reports to the Director, Talent Strategy \& Workforce Transformation.
What you'll do
Performance Strategy \& Global Process Ownership
- Serve as the Global Process Owner for CDW's enterprise performance management process, including philosophy, design, governance, standards, and continuous improvement.
- Set the strategic direction for how CDW sets goals, prioritizes work throughout the year, measures outcomes, and enables meaningful performance conversations.
- Ensure performance practices reinforce CDW's business strategy, values and commitments, and leadership expectations.
- Evaluate how AI, automation, and evolving ways of working should shape the future of performance management and recommend approaches that clarify where people add value and where technology does.
- Partner with HR Technology to design, configure, and continuously improve performance capabilities within Workday and related tools.
- Use data, adoption metrics, and stakeholder feedback to assess effectiveness, simplify the experience, and increase business impact.
HRBP \& Regional Talent Partnership
- Partner closely with HR Business Partners and regional Talent teams to drive consistent, high\-quality execution across the enterprise.
- Balance global consistency with the flexibility needed for regional, functional, and market\-specific requirements.
- Equip leaders and HR teams with guidance, tools, and enablement that improve the quality of performance conversations.
- Serve as a trusted advisor on performance philosophy, governance, technology, and adoption.
AI Activation \& Adoption
- Lead enterprise AI activation efforts that turn AI fluency strategy into adoption and sustained behavior change.
- Lead and coordinate CDW's AI Champion network as a scalable source of enablement, engagement, and feedback.
- Partner with Learning, which owns AI fluency strategy and capability development, to reinforce and amplify enterprise priorities.
- Maintain a clear view of organizational AI fluency, adoption barriers, and enablement opportunities, and bring forward ideas to continue building fluency.
- Demonstrate personal AI fluency and actively apply AI to improve productivity, decision\-making, and outcomes.
What we expect of you
- Bachelor's degree in human resources, Business, Organizational Development, Psychology, or a related field.
- 8\+ years of progressive experience in performance management, talent management, organizational effectiveness, HR business partnership, change management, or a related HR discipline.
- Experience leading enterprise\-wide HR or talent processes, programs, or transformation initiatives within a large, multinational company.
- Experience partnering with business leaders and HR teams to drive adoption and lead change.
- Strong program management, communication, and stakeholder management skills.
- Experience leveraging HR technology and workforce data to improve processes and outcomes.
- Demonstrated personal use of AI tools to improve productivity, analysis, communication, or decision\-making.
- Strong curiosity and commitment to continuous learning in AI, workforce transformation, and the future of work.
Preferred skills, qualities and experience needed
- Experience influencing the strategy, governance, and adoption of a global HR or talent process as a process, program, or product owner within a multinational company.
- Prior HR Business Partner experience, with demonstrated ability to translate business strategy into talent solutions.
- Experience partnering across regions and balancing enterprise consistency with market\-specific needs.
- Experience with Workday or a similar talent technology platform.
- Experience leading change champion, ambassador, super\-user, or community\-based adoption networks to drive adoption
Pay range: $114,000 \- $158,800 depending on experience and skill set
Annual bonus target of 10% subject to terms and conditions of plan
Benefits overview: https://cdw.benefit\-info.com/
Salary ranges may be subject to geographic differentials
CDW is committed to being an AI\-fluent organization
We’re looking for people who bring curiosity, a learner’s mindset, and a willingness to engage with ever\-evolving technology and tools. We value adopting AI as a partner, openness to experimentation, and a shared interest in learning together on AI. Our goal is to create a culture where AI enhances—not replaces—human creativity and decision\-making. You don’t need to be an expert today; what matters is your readiness to explore, adapt, and grow with us as we integrate AI responsibly and effectively into our work.
Additionally, CDW is committed to fostering an equitable, transparent, and respectful hiring process for all applicants. During our application process, our goal is to understand your experience, strengths, skills, and qualifications. As an AI forward company, we see AI not just as a tool, but as a catalyst for new ways of thinking, creating, and communicating. We encourage candidates to embrace an AI mindset, one that’s curious, adaptive, and ready to explore what’s possible. We welcome thoughtful use of AI to expand your perspective and elevate how you share your story, while ensuring your application remains rooted in your own background, judgment, and voice.
About Us
CDW is a Fortune 500 technology solutions provider that helps businesses, government, education, and healthcare organizations achieve what’s possible through technology. What makes CDW different isn’t just what we do—it’s how we do it. At CDW we act as one—building trust, speaking candidly, and working together to achieve more. We play to win—focusing on what matters most and delivering for our customers. And we think forward—staying curious, moving fast, and continuously learning. We believe meaningful work happens when people feel supported, heard, and empowered to contribute. That’s why we think of ourselves as coworkers, not just employees—working together to solve complex challenges and deliver real impact for our customers and communities.
As a full‑stack, full‑lifecycle technology partner, CDW brings deep expertise, strong relationships, and broad industry knowledge to help turn ideas into outcomes. When you join CDW, you become part of a collaborative environment where your work matters, your growth is supported, and your contributions help shape what’s next.
Together, we deliver the full promise of what technology can do. Together, we Make Amazing Happen.
CDW is an equal opportunity employer. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status or any other basis prohibited by state and local law.
Salary Context
This $114K-$158K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CDW, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($136K) sits 37% below the category median. Disclosed range: $114K to $158K.
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.
CDW AI Hiring
CDW has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rosemont, IL, US. Compensation range: $158K - $158K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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