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About This Role
Description
At CDW, we make it happen, together. Trust, connection, and commitment are at the heart of how we work together to deliver for our customers. It’s why we’re coworkers, not just employees. Coworkers who genuinely believe in supporting our customers and one another. We collectively forge our path forward with a level of commitment that speaks to who we are and where we’re headed. We’re proud to share our story and Make Amazing Happen at CDW.
CDW Marketing is building the next generation of B2B marketing powered by AI, insight, creativity, and measurable performance. We are looking for exceptional undergraduate students to join a high impact summer internship program designed to bring fresh perspectives, deliver real business value, and build a strong pipeline of future marketing talent for CDW.
Interns will work on meaningful projects from day one, partner closely with marketing leaders, and contribute to priority initiatives that support active CDW marketing work. Top performers may be considered for a full time opportunity in 2027\.
This internship is intended for ambitious students who want to combine technology, problem solving, and business thinking in a fast moving marketing environment. Rather than rotating across unrelated assignments, each intern will work deeply within the defined focus area listed in the job title while collaborating cross functionally across the broader marketing organization.
What You’ll Do
- Create AI assisted written and video content for campaigns, web, social, thought leadership and sales enablement.
- Build and refine AI prompts, scripts and storyboards that improve creative efficiency and output quality.
- Translate marketing strategies into compelling narratives across formats using emerging AI production tools that support active campaigns.
- Best aligned to students with strengths in writing, video storytelling and AI assisted content creation across multiple formats and channels.
- Contribute to high\-priority summer initiatives that support real CDW marketing outcomes, not simulated assignments.
- Work directly with a Director in Marketing and partner with cross\-functional stakeholders depending on the chosen track.
- Use AI tools, research, creative development, or analytics to improve how CDW plans, creates, measures, and optimizes marketing work.
- Translate ideas into outputs such as prototypes, content, videos, dashboards, insights, recommendations, and documented playbooks.
What We Expect From You
- Currently enrolled Northwestern University undergraduate students with a graduation date of December 2026 through May 2027 with the ability to begin full\-time employment in 2027 (if offered employment post\-internship).
- Major in Marketing Communications, Computer Science, Journalism, Radio/TV/Film, AI, Data Analytics, or a closely related field.
- Strong communication skills, intellectual curiosity, sound judgment, and the ability to operate in a fast\-paced environment.
- Comfort learning and applying modern tools such as Adobe Experience Platform, Power BI, SQL, Python, Google Veo 3\.1, prompt engineering platforms, video editing tools, or closely related technologies depending on track.
- Bias for action, high standards for quality, and a willingness to test ideas, learn quickly, and iterate.
- Ability to commit to a 40\-hour work week during standard business hours throughout the summer, with a hybrid work arrangement.
- Authorization to work in the U.S. without sponsorship as a full\-time coworker.
Optional attachments to include with your application: cover letter, resume, college transcripts, portfolios, project examples, writing samples, dashboards, GitHub links, or creative reels.
Hourly Rate: $20/hour
Benefits overview: https://cdw.benefit\-info.com/
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.
We make technology work so people can do great things.
CDW is a leading multi\-brand provider of information technology solutions to business, government, education and healthcare customers in the United States, the United Kingdom and Canada. A Fortune 500 company and member of the S\&P 500 Index, CDW helps its customers to navigate an increasingly complex IT market and maximize return on their technology investments. Together, we unite. Together, we win. Together, we thrive.
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.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
CDW AI Hiring
CDW has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chicago, IL, US, Vernon Hills, IL, US, Remote, US. Compensation range: $100K - $170K.
Location Context
AI roles in Chicago pay a median of $202,350 across 310 tracked positions. That's 10% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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