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About This Role
*Are you ready for what’s next?*
*Come explore opportunities within* *Brunswick, a global marine leader* *committed to challenging conventions and innovating next\-generation technologies that transform experiences on the water and beyond. Brunswick believes “Next Never Rests™,” and we offer a variety of exciting careers and growth opportunities within united teams defining the future of marine recreation.*
Location: Mettawa, IL
Workplace Category: Hybrid, 3x per week in office
Direct Reports: No
Pay Range: $118,400\-174,000
Visa Sponsorship:*Applicants must be currently authorized to work in the United States. This position is not eligible for employment visa sponsorship now or in the future.*
Innovationis the heart of Brunswick.Seehowyourcontributionswillhelptransform vision into reality:
Position Overview:
We are seeking an AI Operations \& Enablement Lead to join our Marketing Center of Excellence (CoE). This is a unique, high\-impact role that bridges the gap between abstract AI capabilities and real\-world marketing execution. Operating as a centralized peer and partner to our regional brands and business units, you will be responsible for identifying high\-value marketing use cases, prototyping and reviewing AI agent solutions, and physically deploying tactical automations that drive measurable efficiency.
While this role requires a master\-level grasp of systems thinking and cross\-functional program management to navigate our corporate architecture, it is fundamentally a scrappy, builder\-first position. You will not just recommend solutions—you will build, tweak, audit, and run them alongside our brand teams.
At Brunswick, we have passion for our work and a distinct ability to deliver.
Essential Functions:
- 1\. Use Case Discovery \& Solution Auditing (The Systems Thinker)
+ Triage \& Backlog Management: Actively partner with brand and business teams to audit their current workflows, identify bottlenecks, and surface high\-impact AI opportunities (e.g., automated content variation, localized ad ops, predictive lead scoring).
+ Solution Review: Evaluate existing or vendor\-proposed AI tools to ensure they integrate seamlessly with our current MarTech stack without fracturing data workflows or violating security guardrails.
+ Feasibility Guardrails: Filter opportunities into distinct buckets: "Quick wins" (low\-code automation), "Strategic builds" (partnering with Data Science), or "Not viable."2\. Technical Prototyping \& Agent Development (The Builder)
+ Rapid Prototyping: Build and test AI\-driven agents and intelligent automation workflows using APIs and orchestration frameworks approved by Brunswick Corporation.
+ Production Integration: Operationalize and inject these AI prototypes into our core marketing systems (e.g., Marketo, Salesforce, CMS, and dynamic creative platforms) to ensure a frictionless user experience for frontline marketers. Ensure every production workflow has a named business owner and review cadence.
+ Performance Monitoring: Set up basic observability tracking to ensure deployed agents are accurate, cost\-effective, and performing as intended without "hallucination drift."
+ AI Governance: Partner with cross\-functional teams to operationalize responsible\-AI guardrails across all deployed agents including: tool/vendor intake reviews, data\-handling and PII boundaries, prompt and output logging, human\-in\-the\-loop checkpoints, and a documented kill\-switch protocol for any agent that drifts out of tolerance.3\. Tactical Deployment \& Brand Enablement (The Program Manager)
+ Run Agile Sprints: Own the execution roadmap by managing 2\-week agile sprint cycles to move AI solutions rapidly from ideation to active deployment within business units.
+ Playbook Creation: Translate successful deployments into repeatable, documented playbooks, governance decision trees, and self\-service templates so other brands can adopt them autonomously.
+ Hands\-on Hypercare: Embed with brand teams during early deployment phases to troubleshoot, gather immediate feedback, and optimize solutions in real\-time.
+ Adoption: Drive sustained usage by running brand\-embedded enablement programs, office hours, and a marketing AI Champions network across divisions and COE.
+ Community Building: Lead onboarding for new tools and proactively identify low\-adoption pockets to coach teams through resistance, skill gaps, or workflow friction.
+ Prompting: Structure, standardize and continuously refine a shared prompt and pattern library.4\. Reporting \& Value Realization
+ Transparency: Own the AI COE value narrative by building and maintaining a leadership\-ready dashboard that tracks adoption metrics (active users, % of teams onboarded), efficiency outcomes (hours saved, cycle\-time compression), and solution health (accuracy, incident counts, etc.).
+ Socialization: Deliver a monthly scorecard and quarterly business review that translates technical output into measurable enterprise impact for marketing leadership and the broader executive team.
Diversityofthought and experiencesisfundamental whenimaginingthe unimaginable.Certain skillsets/experiencesare necessary;however,others can be developedalong the way.
Required Qualifications:
- + Experience: 5–8 years blending Marketing Operations, Technical Program Management (TPM), Product Operations, or MarTech engineering.
+ Recent Hands\-On AI Building: Must have a proven track record of actively building with current AI tools within the past 12 months (e.g., custom agent orchestration, API\-driven workflows, or multi\-step LLM automations). *Be prepared to walk through a workflow you built.*
+ Systems \& Architecture Fluency: A strong conceptual grasp of how enterprise data flows, how APIs connect, and how AI overlays across a fragmented modern marketing stack.
+ MarTechFamiliarity: Solid working knowledge of enterprise CRMs, marketing automation platforms, and ad platforms (e.g., Salesforce, Adobe/Marketo, Google/Meta Ads infrastructure).
+ The "Scrappy Translator" Mindset: Exceptional communication skills with the ability to explain complex LLM behaviors to a traditional brand manager, coupled with a self\-directed work style that thrives on "figuring it out" in ambiguous environments.
What Success Looks Like in 6 Months:
- + You have successfully audited our top 3 prioritized functional capability areas and established a clear, prioritized backlog of AI operational use cases.
+ You have built, tested, and deployed at least 4 custom AI workflows or agent solutions that compress marketing production time by 30% or greater.
+ You have built a foundational "Marketing AI Playbook" that allows teams to self\-vet ideas before bringing them to the CoE.
+ You have achieved 40% active adoption across the top 3 prioritized initiatives, measured by weekly active users of deployed workflows.
The anticipated pay range for this position is $118,400\-174,000 annually. The actual base pay offered will vary depending on multiple factors including job\-related knowledge/skills, relevant experience, business needs, and geographic location. In addition to base pay, this position is eligible for an annual discretionary bonus.
At Brunswick, it is not typical for an individual to be hired at or near the top end of the salary range for their role. Compensation decisions are dependent upon the specifics of the candidate’s qualifications and the business context.
This position is eligible to participate in Brunswick's comprehensive and high\-quality benefits offerings, including medical, dental, vision, paid vacation, 401k (up to 4% match), Health Savings Account (with company contribution), wellbeing program, product purchase discounts and much more. Details about our benefits can be found here.
Why Brunswick:
Whatever tomorrow brings, we’ll be at the leading edge. As the clear leader in the marine industry, we’re committed to our values and supporting our exceptional people. We offer and encourage growth opportunities within and across our many brands. In addition, we’re proud of being recognized for making a splash with numerous awards!
*Next is Now!*
*We value growth and development, recognizing that people come with a wealth of experience and talent beyond just the technical requirements of a job. If your experience is close to what you see listed here, please still consider applying.*
Brunswick is an Equal Opportunity Employer and considers all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status or any other characteristic protected by federal, state, or local law. Diversity of experience and skills combined with passion is key to innovation and inspiration and we encourage individuals from all backgrounds to apply. If you require accommodation during the application or interview process, please contact [email protected] for support.
For more information about EEO laws, \- click here
Brunswick Corporation participates in E\-Verify as part of our commitment to a lawful and transparent hiring process. For additional information click here: https://www.brunswick.com/e\-verify.
Brunswick and Workday Privacy Policies
Brunswick does not accept applications, inquiries or solicitations from unapproved staffing agencies or vendors. For help, please contact our support team at: [email protected] or 866\-278\-6942\.
All job offers will come to you via the candidate portal you create when applying through a posted position through https:///www.brunswick.com/careers. If you are ever unsure about what is being required of you during the application process or its source, please contact HR Shared Services at 866\-278\-6942 or [email protected].
\#Brunswick Corporation
Salary Context
This $118K-$174K 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 Brunswick, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($146K) sits 19% below the category median. Disclosed range: $118K to $174K.
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.
Brunswick AI Hiring
Brunswick has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mettawa, IL, US. Compensation range: $174K - $174K.
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.
Frequently Asked Questions
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