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About This Role
Innovate in Charlotte
Thank you for dedicating your time and talent to Lowe’s. We want to give you more opportunities to learn and grow, so if you find a position you’re interested in below, we encourage you to apply!
Your Impact
The primary purpose of this role is to lead artificial intelligence (AI) engineering initiatives and the development of AI solutions that enable the integration and evolution of advanced machine learning (ML) models, including deep learning (DL) frameworks and large language models (LLMs). The Lead AI Engineer will be responsible for leading the advancement of model performance, implementing essential tools and frameworks, and optimizing compute and storage solutions to elevate AI technologies to new heights.
Work with a Winning Team
As part of a Fortune 500 company and retail leader, your work can change an entire industry. Our CEO is a forward\-thinker when it comes to tech, and with one of Forbes Top 50 CIOs leading the charge, you can come to work knowing you’ll have access to the data, tools, and support that few other companies can offer. We also know what it takes to create an inclusive culture that supports you. Our teams are structured around the engineer, giving you the support you need to do your best work. Since we’ve been in business for over 100 years, we’ve built an excellent track record of growth and success. There’s peace of mind knowing you have the stability and resources you need to focus on solving tough challenges. And as you solve these challenges, know you’ll be surrounded by supportive associates with curious minds who listen to you, respect you, and recognize your hard work.
What You Will Do
- Leads the development of the AI platform and ensures the scalability of the platform accommodates a wide spectrum of AI models and integration of new AI advancements as APIs.
- Leads the architectural design, development, and implementation of sophisticated tools and frameworks that elevate ML experimentation and deployment, ensuring that the platform delivers high\-end AI functionalities.
- Architects and orchestrates the management of graphics processing unit and central processing unit resources to enhance the performance of AI models while maximizing cost efficiency and resource utilization.
- Mentors and guides junior team members to ensure a seamless integration of AI models into the platform.
- Leads the creation and management of robust data movement strategies and pipelines, optimizing data flows to meet the requirements of intensive AI model training and inference processes.
- Leads initiatives for continuous improvement by evaluating platform performance analytics and gathering user feedback.
- Leads cross\-functional teams, fostering a collaborative environment that merges deep technical knowledge with strategic business insights and a keen understanding of user needs.
- Champions the development and enforcement of rigorous security protocols and governance measures for the AI platform, ensuring data integrity and adherence to industry standards and best practices.
Minimum Qualifications
- Bachelor’s degree in science, technology, engineering, math, or related field or equivalent years of experience in lieu of education requirement, if applicable
- 6 years of experience executing and deploying data science, ML, and DL (fewer years may be accepted with a Master's or Doctorate degree), ML platform engineering, MLOps tools, and ML frameworks
- 6 years of programming experience in Java and/or Python
- 4 years of experience working with cross\-functional partners
Preferred Skills/Education
- Master’s degree in science, technology, engineering, math, statistics, physics, economics, data science, information science, or quantitative analytics
- 5 years of experience in SQL and NoSQL databases, Hadoop ecosystem, Druid, Trino, Big Query, Google Vertex AI
- 6 years of Python programming experience.
- 2 years of experience in AI technology.
Benefits
- 401k with up to 4\.25% match
- Discounted Employee Stock Purchase Plan (15% discount of strike price)
- Tuition\-Free Education
- 10\-week Maternity/Parental Leave
- 10% Associate Discount
For information about our benefit programs and eligibility, please visit https://talent.lowes.com/us/en/benefits
About Lowe’s
Lowe’s Companies, Inc. (NYSE: LOW) is a FORTUNE® 100 home improvement company with total fiscal 2025 sales of more than $86 billion. Lowe’s employs approximately 300,000 associates and operates over 1,750 home improvement stores, 540 branches and 120 distribution centers. Based in Mooresville, N.C., Lowe’s supports the communities it serves through programs focused on creating safe, affordable housing, improving community spaces, helping to develop the next generation of skilled trade experts and providing disaster relief to communities in need. For more information, visit Lowes.com.
*Lowe’s is an equal opportunity employer and administers all personnel practices without regard to race, color, religious creed, sex, gender, age, ancestry, national origin, mental or physical disability or medical condition, sexual orientation, gender identity or expression, marital status, military or veteran status, genetic information, or any other category protected under federal, state, or local law.*
Pay Range: $128,300\.00 \- $243,800\.00 annually Starting rate of pay may vary based on factors including, but not limited to, position offered, location, education, training, and/or experience. For information regarding our benefit programs and eligibility, please visit https://talent.lowes.com/us/en/benefits.
Salary Context
This $128K-$243K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Lowe's Home Improvement, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($186K) sits 15% below the category median. Disclosed range: $128K to $243K.
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.
Lowe's Home Improvement AI Hiring
Lowe's Home Improvement has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US. Compensation range: $243K - $243K.
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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