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About This Role
Company Summary
Zoro.com is a leading eCommerce platform offering nearly 12 million tools, parts and supplies for our business customers. Launched in 2011, we brought a B2C\-like experience to the B2B industry, and continue to be at the forefront of digital innovation at the intersection of technology and distribution. We have rapidly grown to over $1 billion in annual revenue and we're just getting started!
Job Summary
This role drives end\-to\-end order fulfillment performance by owning measurement frameworks and identifying systemic drivers of customer experience outcomes. You will deliver actionable insights that enable the business to prioritize and improve order flow, inventory availability, and fulfillment reliability, partnering cross\-functionally to scale data\-driven decision making and support high\-impact supply chain initiatives.
Duties and Responsibilities
- Own and evolve order fulfillment performance measurement by investigating how key metrics are defined and calculated, developing improved frameworks that provide accurate and actionable visibility into performance across the order lifecycle.
- Analyze end\-to\-end fulfillment performance across order management, inventory availability, demand trends, and shipment execution to identify trends, quantify impact, and uncover systemic drivers of customer experience issues.
- Lead analysis and execution support for key supply chain initiatives, including bringing items into stock, improving in\-stock rates, and planning for demand variability to enhance availability and customer satisfaction.
- Navigate ambiguous problem spaces to define and structure problems by identifying gaps in current understanding, forming hypotheses, and developing analytical approaches to uncover root causes and new opportunity areas.
- Generate actionable insights that enable targeted intervention by identifying operational patterns that drive negative outcomes (e.g., delays, cancellations, backorders, inventory gaps).
- Partner cross\-functionally with Operations, Inventory, Category Management, Finance, Customer Service, and Technology teams to prioritize insights, align on solutions, and drive execution of process improvements.
- Drive cross\-functional alignment and decision\-making by leading deep\-dive analyses into high\-impact issues, communicating clear recommendations, and influencing stakeholders to take action aligned with business priorities.
- Improve scalability and data reliability of fulfillment analytics by developing reporting solutions, automating processes, and leveraging advanced analytics tools to enhance insight generation and operational efficiency.
- Support high\-visibility initiatives by connecting insights across order management, inventory, and fulfillment operations, ensuring alignment with Zoro's priorities around availability, speed, reliability, and customer experience.
Minimum Qualifications
- Bachelor's degree in Business, Supply Chain, Finance, or a related field required.
- 5\+ years of experience in supply chain, fulfillment, operations, or customer order management.
- Demonstrated ability to lead cross\-functional initiatives and drive outcomes in ambiguous problem spaces.
- Strong analytical and problem\-solving skills, with experience using data to influence decisions and improve processes.
- Excellent verbal and written communication skills with the ability to translate complex analyses into clear business insights.
Preferred Qualifications
- Experience working with order management, inventory planning, demand planning, or fulfillment operations.
- Advanced proficiency in Excel/Google Sheets and experience with BI tools such as Looker, Tableau, or Power BI.
- Working knowledge with ERP or order management systems like SAP or NetSuite.
- Experience with SQL, data modeling, and automation tools, including applying AI/ML solutions to scale analytics, improve decision\-making, and drive operational efficiency.
- Strong critical thinking skills with a continuous improvement mindset; Lean/Six Sigma experience is a plus.
- Proven ability to manage multiple workstreams and prioritize effectively in a fast\-paced environment.
- Experience in E\-commerce or direct\-to\-customer fulfillment.
Our Culture
Zoro was founded in 2011 with a simple idea: make it easy for businesses to get the tools, parts, and supplies they need to keep things running. We've grown by staying curious, moving quickly, and solving everyday challenges in smart, practical ways. Backed by W.W. Grainger and inspired by our endless assortment business model, we're on a clear path toward our next big milestone: $2 billion in revenue—and beyond.
At Zoro, we don't just follow a playbook—we help build it. You'll get to work on real problems with a supportive team that shares ideas freely, learns from each other, and celebrates wins together. Our culture is grounded in values that guide how we show up every day: Winning \& Learning Together, Being Customer Obsessed, Being Transparent, and Taking Ownership. We don't have all the answers, but we're always asking good questions.
Zoro's culture has been recognized by Fortune, Best Places to Work, and Built In Chicago—but the recognition we care about most comes from our team members, who make this place what it is.
We also know that flexibility matters. Our hybrid work model gives you space to focus and the flexibility to live your life — asking team members to be onsite at least two days a week. Our Chicago HQ (right above Ogilvie Transportation Center in the Accenture Tower) is always open and ready for connection, collaboration, or just a good cup of coffee.
At Zoro, we're growing fast toward big aspirations — and we're continuously excited about the new challenges we get to solve together.
*We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, protected veteran status, or any other protected characteristic under federal, state, or local law. We are proud to be an equal opportunity workplace. We are also committed to fostering an inclusive, accessible work environment that includes both providing reasonable accommodations to individuals with disabilities during the application and hiring process as well as throughout the course of one's employment. Should you need a reasonable accommodation during the application and selection process, including, but not limited to use of our website, any part of the application, interview, or hiring process, please advise us so that we can provide appropriate assistance.*
Salary Context
This $71K-$119K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Zoro, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($95K) sits 43% below the category median. Disclosed range: $71K to $119K.
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.
Zoro AI Hiring
Zoro has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $119K - $119K.
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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