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About This Role
As a family company, we serve people and communities. When you work at Meijer, you’re provided with career and community opportunities centered around leadership, personal growth and development. Consider joining our family – take care of your career and your community!
Meijer Rewards
- Weekly pay
- Scheduling flexibility
- Paid parental leave
- Paid education assistance
- Team member discount
- Development programs for advancement and career growth
Please review the job profile below and apply today!
Position will follow our hybrid schedule: Monday\-Wednesday in Grand Rapids MI Corporate office, Thursday\-Friday remote. What You'll be Doing:
Data Engineering, Analytics \& AI/Automation
- Lead the design, development, and implementation of data engineering, analytics, and AI/automation solutions to support business objectives.
- Oversee data architecture, ensuring data integrity, security, and scalability.
- Manage and mentor a team of data engineers, data scientists, and analysts, fostering a culture of collaboration and continuous improvement.
- Collaborate with cross\-functional teams to identify data needs and develop strategies to leverage data for business insights and decision\-making.
- Drive adoption of best practices in data management, analytics, and AI/automation.
- Ensure compliance with data governance policies and regulations.
- Stay current with industry trends and emerging technologies in data engineering, analytics, and AI/automation.
- Develop and manage budgets, resources, and timelines for data projects.
- Ensure all teams follow engineering and IT standards for change controls and IT practices for production systems.
Enterprise Quality Adoption
- Own the enterprise quality strategy — embed quality into the software development lifecycle, not onto it.
- Drive adoption of test automation, shift\-left testing, and continuous quality practices across all engineering teams.
- Define and enforce quality standards, frameworks, and tooling across the portfolio; ensure consistent adoption at scale.
- Partner with engineering and product teams to establish quality gates that protect production stability without slowing delivery.
- Report on quality health across domains, with clear visibility into defect rates, test coverage, and release readiness.
Engineering Delivery Performance — DORA Metrics
- Establish DORA metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, Mean Time to Recovery) as the standard measurement framework for engineering delivery health.
- Own the baseline, targets, and reporting cadence for DORA metrics across teams; surface trends to senior leadership with clear business context.
- Use DORA data to identify delivery bottlenecks, prioritize platform and process investments, and demonstrate improvement over time.
- Connect engineering performance to business outcomes — faster delivery and lower failure rates translate directly to customer experience and cost efficiency at Meijer's scale.
- Partner with DevOps and platform teams to build the tooling and observability infrastructure required to measure and improve DORA outcomes.
IT General Controls (ITGC)
- Accountable for ITGC compliance across the technology domains in scope — change management, access controls, computer operations, and program development controls.
- Partner with Internal Audit, Compliance, and Finance to ensure controls are designed, operating effectively, and audit\-ready.
- Own remediation of ITGC deficiencies; drive root cause analysis and sustainable control improvements rather than point\-in\-time fixes.
- Ensure all teams understand and operate within ITGC requirements as a standard part of the delivery process — not a compliance afterthought.
- Maintain documentation, evidence, and control narratives sufficient to support SOX and internal audit cycles.
What You Bring with You (Qualifications):
Education
- Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field. Master's degree preferred.
Experience
- 10\+ years of experience in data engineering, analytics, and AI/automation, with at least 5 years in a leadership role.
- Proven experience establishing and scaling enterprise quality practices across large engineering organizations.
- Hands\-on experience implementing DORA metrics programs and using delivery performance data to drive engineering improvement.
- Demonstrated experience with ITGC compliance, SOX controls, or equivalent control frameworks in an enterprise environment.
- Track record of managing multiple complex programs simultaneously in a fast\-paced, high\-scale environment.
Technical Skills
- Strong knowledge of data architecture, data warehousing, ETL processes, and data modeling.
- Proficiency in Python, Java, or Scala; experience with big data technologies including Spark, Kafka, and Databricks.
- Expertise in machine learning and AI frameworks (TensorFlow, PyTorch, scikit\-learn or equivalent).
- Familiarity with CI/CD tooling, test automation frameworks, and observability platforms used to track delivery and quality metrics.
- Working knowledge of ITGC control domains: logical access, change management, computer operations, and program development.
Leadership \& Communication
- Strong communication and interpersonal skills; able to collaborate with and influence stakeholders at all levels.
- Speaks the language of business outcomes — connects technology performance to cost, revenue, and customer experience.
- Proven ability to manage multiple priorities and drive accountability across matrixed teams.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Meijer, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Meijer AI Hiring
Meijer has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Grand Rapids, MI, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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