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About This Role
Date: Jun 9, 2026
Location: LAKE FOREST, IL, US, 60045\-5203
Company: Grainger Businesses
Work Location Type: Remote
Req Number 330464
About Grainger
W.W. Grainger, Inc. is a leading broad line distributor with operations primarily in North America and Japan. At Grainger, We Keep the World Working® by serving more than 4\.6 million customers worldwide with maintenance, repair and operating (MRO) products and value\-added solutions delivered through innovative technology and deep customer expertise. Known for its commitment to service and purpose\-driven culture, the Company reported 2025 revenue of $17\.9 billion. For more information, visit www.grainger.com.
Compensation
The anticipated base pay compensation range for this position is $148,900\.00 – $248,200\.00\. This role is eligible for an incentive target of up to 15 based on the achievement of individual and company performance objectives in accordance with the current terms of the incentive program which are subject to change.
Sponsorship
This position is not eligible for any form of sponsorship now or in the future. Individuals requiring sponsorship (e.g. OPT or H1B visa status) should not apply. Only individuals authorized to work in the United States now and for the foreseeable future will be considered for this position.
Rewards and Benefits
With benefits starting on day one, our programs provide choice and flexibility to meet team members' individual needs, including:
- Medical, dental, vision, and life insurance plans with coverage starting on day one of employment and 6 free sessions each year with a licensed therapist to support your emotional wellbeing.
- 18 paid time off (PTO) days annually for full\-time employees (accrual prorated based on employment start date) and 6 company holidays per year.
- 6% company contribution to a 401(k) Retirement Savings Plan each pay period, no employee contribution required.
- Employee discounts, tuition reimbursement, student loan refinancing and free access to financial counseling, education, and tools.
- Maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and up to 4 weeks paid leave for non\-birth parents.
For additional information and details regarding Grainger’s benefits, please click on the link below:
https://experience100\.ehr.com/grainger/Home/Tools\-Resources/Key\-Resources/New\-Hire
Grainger Benefits
The pay range provided above is not a guarantee of compensation. The range reflects the potential base pay for this role at the time of this posting based on the job grade for this position. Individual base pay compensation will depend, in part, on factors such as geographic work location and relevant experience and skills.
The anticipated compensation range described above is subject to change and the compensation ultimately paid may be higher or lower than the range described above.
Grainger reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion at any time, consistent with applicable law.
Position Details
Grainger’s Sales Machine Learning team is seeking a Staff Applied Machine Learning Scientist to serve as a technical leader for Generative AI and agentic AI applications across the Sales domain. In this role, you will help design and build Grainger’s Sales AI platform, enabling 3,000\+ sellers to work more productively, serve customers more effectively, and drive measurable business growth.
You will lead the development of scalable AI systems that combine LLMs, machine learning models, retrieval, tools, APIs, workflow orchestration, evaluation, and monitoring. The work focuses on building production\-ready virtual assistants and deep agents across mobile and web experiences. You will set the technical direction, establish patterns for reliable and governed AI systems, mentor engineers, and partner with product, engineering, design, and business stakeholders to turn high\-value Sales opportunities into production\-ready AI capabilities.
The work location is Chicago, IL on a hybrid basis (8 days per month). We will consider highly qualified candidates who are remote, provided they can come onsite during orientation and travel for team meetings, estimated at once per quarter.
You Will
- Design, develop, and maintain both traditional and cutting\-edge machine learning models that solve real\-world business problems—ensuring scalability, efficiency, and measurable impact
- Develop applied GenAI capabilities across web and mobile experiences, including seller\-facing digital assistants, conversational Q\&A, task automation, summarization, workflow guidance, recommendations, and next\-best\-action intelligence.
- Design AI systems with appropriate controls for reliability, privacy, security, governance, observability, and human oversight.
- Collaborate cross\-functionally with ML scientists, data/platform/software engineers, UX designers, and product managers to seamlessly integrate models into production systems
- Establish and champion best practices for model development, deployment, and lifecycle management. Clearly articulate technical concepts and create visual representations to communicate your work and its impact
- Mentor and empower engineers and scientists, fostering a culture of continuous learning and shared growth. Share knowledge through internal documentation, presentations, and external community engagement
- Translate customer and product requirements into ML strategies that deliver tangible, data\-driven outcomes
- Contribute to robust, maintainable codebases and scalable ML infrastructure that supports long\-term innovation
- Stay at the forefront of research and technology, continuously evaluating and applying emerging methods that add value to the organization
You Have
- Master's degree in computer science, data science, statistics, mathematics, or a closely related field
- 5\+ years of experience designing and building scalable AI/ML applications and systems in cloud environments.
- Experience developing virtual assistants, digital assistants, conversational AI applications, agentic workflows, recommendation systems, or AI\-powered decision\-support tools.
- Production\-grade implementation experience using PyTorch, HuggingFace, MLFlow
- Experience using frameworks to build LLM\-based applications or agentic workflows, such as LangGraph, LangSmith, LangChain, LlamaIndex, or similar.
- Experience designing AI evaluation systems, including offline evaluation, online monitoring, hallucination and quality checks, latency measurement, user feedback loops, and business impact tracking.
- Experience with the end\-to\-end software development lifecycle, including CI/CD pipelines, Kubernetes\-based deployments, testing, monitoring, alerting, backend systems, APIs, and production support.
- Strong Python fundamentals, with experience using modern ML, data, and LLM application development libraries.
- Ability to provide technical leadership, influence architecture, mentor team members, and communicate complex AI/ML concepts to technical and non\-technical audiences.
Preferred
- PhD computer science, data science, statistics, mathematics, or a closely related field
- Experience with deep agent or multi\-agent architectures, including planning, reasoning, tool use, tool dispatch, error recovery, session state, memory, sub\-agent coordination, and feedback loops.
- Experience designing AI agent skill systems, including reusable capability packages, skill registries, versioning, security vetting, governance controls, and lifecycle management.
- Experience building digital assistant experiences for web, mobile, CRM, seller productivity, customer support, or enterprise workflow applications.
- Production model serving experience with vLLM, Triton Inference Server, TensorRT, Ray Serve, TorchServe, or similar low\-latency serving infrastructure.
*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 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 $148K-$248K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Grainger, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($198K) sits 7% above the category median. Disclosed range: $148K to $248K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Grainger AI Hiring
Grainger has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lake Forest, IL, US. Compensation range: $248K - $248K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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