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About This Role
Description
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Crain Communications is already well into its AI journey. We've built our own RAG\-based product for customers, developed internal AI tools across multiple departments, and have people throughout the business building new things every day. AI is not new here; it's already woven into how we work.
What we need now is intentionality. Things are moving fast and the opportunity is real, but to scale this across the whole company we need to formalize AI enablement as a function, move from individual experimentation in silos to cohesive, structured execution, and ensure we're doing this with clear guardrails, consistent processes, and a deliberate strategy for where we go next. The goal is bigger than efficiency gains. We're building toward a version of Crain that actually understands itself, where decisions, knowledge, workflows, and institutional memory are captured, connected, and made continuously available to inform how we work and where we're headed.
That's what this role is. The Director, Enterprise AI Enablement will approach this work with a consultative mindset, embedding with teams across the business to understand their world before prescribing solutions. They will own identifying where AI can have the most impact by engaging directly with people throughout the business and distilling that into clear opportunities; setting the policy and guardrails that ensure we're adopting AI in a safe and thoughtful way; and educating the workforce and driving real, lasting adoption across the whole company. This role will have at least one direct report from the start, with the expectation that the team will grow as the function matures.
You'll work across Editorial, Sales, Marketing, HR, Finance, Technology, and Product to find where knowledge is siloed, where decisions are slowed by friction, and where the right AI tooling can unlock real capability. Then you'll build it, sometimes hands\-on, sometimes by working through and alongside the teams closest to the problem.
Key responsibilities:
- Bring a consultative approach to every engagement, taking the time to understand a team's workflows, challenges, and goals before identifying where AI can make a real difference
- Partner with leaders across all major functions to surface workflow friction, knowledge gaps, and high\-value opportunities where AI tools or training can make a meaningful difference
- Own and drive the enterprise AI enablement roadmap, scoping, prioritizing, and executing a portfolio of initiatives from discovery through adoption and measurement
- Define and maintain company\-wide AI policy and guardrails, ensuring Crain is adopting AI in a way that is responsible, secure, and aligned with our values and obligations
- Lead the effort to capture and centralize Crain's institutional knowledge (decisions, processes, data, and expertise) into AI\-powered knowledge bases that make the whole organization smarter: HR resources, customer research, sales and marketing materials, process documentation, and more
- Partner with IT Services on the company\-wide rollout of Claude Enterprise, driving adoption and ensuring teams are set up to get real value from the platform within our Microsoft 365 environment
- Identify and implement agentic workflows and automation that reduce manual overhead across key business functions
- Build a structured approach to connecting Crain's core data sources (Naviga, Salesforce, ArcXP, Smartsheet, Fireflies, AWS, and others) as inputs into a coherent, AI\-accessible model of how the business operates
- Implement and manage a company\-wide AI micro\-learning platform, rolling it out in deliberate phases starting where it will have the most impact, tracking adoption and outcomes at each stage to learn, iterate, and expand intelligently across the organization
- Stay close to advancements in the AI space, continuously evaluating new tools, models, and capabilities; find regular, structured ways to surface and communicate relevant opportunities to stakeholders across Crain so we're always making informed decisions about what to explore, pilot, or adopt
- Maintain a clear focus on cost efficiency and ROI across all AI initiatives; every tool evaluated, every workflow built, and every platform adopted should be held to a standard of delivering measurable value for the business
What you'll bring:
- A consultative mindset; you listen before you prescribe, ask the right questions, and earn trust by understanding people's work before trying to change it
- Hands\-on experience implementing AI tools and workflows inside an organization; you've actually done this, not just theorized about it
- Deep familiarity with Claude and the Anthropic ecosystem, including how to get the most out of it in an enterprise context
- Practical experience designing and deploying agentic workflows; you understand how to connect systems, automate multi\-step processes, and build solutions that run with minimal human intervention
- Proven ability to drive strategic initiatives, influence without authority, and earn trust across an organization
- Strong instincts for discovery; you know how to ask the right questions and turn ambiguous problems into clear, scoped work
- Comfort working across technical and non\-technical stakeholders; you can talk to engineers and align a department head in the same afternoon
- An understanding of how data flows through an organization and why connecting those flows matters
- A bias toward outcomes over outputs; you care whether it worked, not just whether it shipped
Experience:
- Demonstrated track record leading digital transformation or AI adoption initiatives inside a mid\-to\-large organization
- Direct, hands\-on experience with Claude, ideally Claude Enterprise, including deploying it across teams, building Projects and knowledge bases, and driving measurable adoption
- Experience designing and implementing agentic workflows that connect enterprise systems and reduce manual processes
- Background working cross\-functionally at a senior level, translating organizational needs into AI\-powered solutions that stick
- Familiarity with change management in the context of technology adoption; you know how to bring skeptics along and build lasting habits, not just launch tools
- Experience evaluating, piloting, and recommending AI tools and platforms in a business context
- Prior work in media, publishing, or a similarly complex multi\-brand or multi\-business\-unit environment is a plus
Skills:
*Technical*
- Claude and the Anthropic platform (Claude Enterprise, Projects, Cowork, Code, etc.)
- Agentic workflow design and implementation
- Experience working with SaaS integrations, APIs, and MCP connectors to connect enterprise systems and extend AI capabilities
- Prompt engineering and AI system design
- Deep understanding of data pipelines and how enterprise data sources connect
*Soft skills*
- Consultative mindset; you listen to understand before moving to solutions
- Executive\-level stakeholder management and communication
- Ability to translate ambiguity into structured, actionable plans
- Curiosity and a habit of continuous learning
- Comfort with change and the ability to bring others along through it
- Collaborative by default, decisive when it matters
Location: Detroit preferred withChicago or Manhattan offices also possible. In office 3 days per week.
This position is exempt under the Fair Labor Standards Act and is not eligible for overtime pay.
Pay Transparency Disclosure:
The estimated salary range for this position is $140,000 to $160,000\.
The final salary offering will take into account a wide range of factors, including experience, accomplishments and location. The salary range provided should not be considered as a salary limit or cap. In addition to base salary, Crain also offers competitive benefits including retirement plan savings contributions and bonus opportunities based on individual and company performance.
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About Crain Communications:
Crain Communications is a leading business news and information company with a portfolio of 24 media brands that provide indispensable coverage and data for professionals globally and across sectors, including advertising, automotive, finance, healthcare, staffing, and workforce solutions. Many of Crain’s brands are the most influential media properties in the industries and communities they serve, including Ad Age, Automotive News, Pensions \& Investments, Modern Healthcare, Staffing Industry Analysts, as well as Crain’s regional business brands. For more than a century, our dedication to deep sector expertise and journalistic integrity has enabled us to provide trusted insights across all our platforms, empowering today’s business leaders to make industry\-shaping decisions. To learn more about Crain Communications, visit crain.com.
Environmental Demands
Where you work matters. The job posting will provide specific information on where and when your amazing work would be performed. Employee work location is determined by the needs of the specific team and may include on\-site, hybrid or remote. Employee work location is subject to change.
- An “in\-office” role would require the employee to come into the office most days with occasional flexibility to work remotely if tasks can be performed elsewhere and if the manager approves.
- A “remote” role would allow an employee to work from a home office that is in one of the states Crain does business in. We can only employ a remote / "work from home" employee if they reside in one of these states: AZ, CA, CO, FL, GA, IL, MD, MA, MI, MN, NV, NY, NC, OH, OR, TN, TX, VA, WA, WI, and Washington, DC.
- A “hybrid” role would be a mix of in\-office and remote work. There may be a specified schedule for coming into the office or it could be at the discretion of the employee with the manager’s approval, subject to change.
- Employees who live within a reasonable commute distance from a Crain office are expected to work on\-site 3 days per week.
Many positions will also include work done in “the field.” Depending on the role, this may include conducting in\-person interviews, attending work\-related events, meeting with sources or clients. Specifics will be noted in the job posting but are subject to change as a role evolves. Employees may be exposed to adverse environmental conditions, specifically during field work. Other typical job functions are performed under conditions such as those found in general office work.
Travel to cover news stories/events, meetings with clients, and to our geographically separated offices may be required. It is the nature of many positions to experience non\-standard working hours and be on\-call when needed for responding to email, meeting with clients, attending work\-related events, story development or breaking news. Most employees perform work Monday through Friday, although early\-morning, evening or weekend shifts may be required. Work schedule and travel requirements are subject to change as a role and needs evolve over time.
Physical Demands
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of many Crain jobs and are subject to change.
Physical activities will include frequent in\-person or virtual interactions. For most positions, it is essential to be able to remain at a desk/computer workstation for prolonged periods, perform computer\-related tasks, and create/maintain documents within filing systems. Must have close visual acuity to perform an activity, such as preparing and analyzing reports and information, transcribing, viewing a computer terminal, or extensive reading. The typical physical requirements are light work—exerting up to 25lbs of force occasionally and/or up to 10lbs of force frequently and may include climbing, pushing, standing, hearing, walking, reaching, grasping, kneeling, stooping, and repetitive motion. Some positions will have additional physical requirements, including exerting up to 50lbs of force to move and/or carry equipment, supplies, files, or other materials as the role requires.
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential job functions and meet the environmental and physical demands of the role.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60\-1\.35(c)
Salary Context
This $140K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Crain Communications, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($150K) sits 17% below the category median. Disclosed range: $140K to $160K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Crain Communications AI Hiring
Crain Communications has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Detroit, MI, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
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