Interested in this AI/ML Engineer role at Sherwin-Williams?
Apply Now →About This Role
The Lead Machine Learning Engineer is responsible for designing, developing, testing and operationalizing ML (Machine Learning) and other data solutions, including cloud-based or internet-related tools for internal or external consumption. The Incumbent will provide technical solutions to enable the company to meet its strategic objectives through effective collaboration with business analysts, data scientists, software engineers, platform engineers, quality assurance testers, and project managers. The Incumbent will collaborate with others to ensure successful teamwork, effective development, and delivery of high-quality machine learning and data solutions that include MLOps practices such as CI/CD pipelines, automated testing, and monitoring.
At Sherwin-Williams, our purpose is to inspire and improve the world by coloring and protecting what matters. Our paints, coatings and innovative solutions make the places and spaces in our world brighter and stronger. Your skills, talent and passion make it possible to live this purpose, and for customers and our business to achieve great results. Sherwin-Williams is a place that takes its stability, growth and momentum and translates it to possibility for our people. Our people are behind the strength of our success, and we invest and support you in:
Life … with rewards, benefits and the flexibility to enhance your health and well-being
Career … with opportunities to learn, develop new skills and grow your contribution
Connection … with an inclusive team and commitment to our own and broader communities
It's all here for you... let's Create Your Possible
At Sherwin-Williams, part of our mission is to help our employees and their families live healthier, save smarter and feel better. This starts with a wide range of world-class benefits designed for you. From retirement to health care, from total well-being to your daily commute—it matters to us. A general description of benefits offered can be found at http://www.myswbenefits.com/. Click on “Candidates” to view benefit offerings that you may be eligible for if you are hired as a Sherwin-Williams employee.
Compensation decisions are dependent on the facts and circumstances of each case and will impact where actual compensation may fall within the stated wage range. The wage range listed for this role takes into account the wide range of factors considered in making compensation decisions including skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. The wage range, other compensation, and benefits information listed is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable federal, state, and local laws including with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act where applicable.
Sherwin-Williams is proud to be an Equal Employment Opportunity employer. All qualified candidates will receive consideration for employment and will not be discriminated against based on race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, pregnancy, genetic information, creed, marital status or any other consideration prohibited by law or by contract.
As a VEVRAA Federal Contractor, Sherwin-Williams requests state and local employment services delivery systems to provide priority referral of Protected Veterans.
Please be aware, Sherwin-Williams recruiting team members will never request a candidate to provide a payment, ask for financial information, or sensitive personal information like national identification numbers, date of birth, or bank account numbers during the application process.
- Oversee the design, development, and deployment of machine learning models and other data solutions in alignment with business requirements and technical specifications defined
- Guide the training, validation, and optimize machine learning models and other data solutions to ensure high performance and accuracy. Implement techniques for model/solution improvement and fine-tuning.
- Provide strategic direction in the writing of high-quality, maintainable, and scalable code
- Expert in Creating and maintaining technical documentation including model development processes, deployment workflows, operational procedures, code comments, design documents, and user manuals
- MLOps Implementation: Provide expert guidance through the development and implementation of CI/CD pipelines for seamless model deployment. Guide testing throughout the development, integration, deployment, and management of the software solution, and automating where possible to ensure solution quality and security in accordance with industry standards and company policies
- Foster cross functional collaboration with data scientists, business analysts, software engineers, IT operations, quality assurance testers, and project managers to to translate business requirements into successful technical solutions that are integrated into production environments
- Provide ongoing solution support including incident and problem management, root cause analysis, request fulfilment, security compliance, fault repair, resiliency testing, and observability
- Innovation and Research: Stay updated with the latest advancements in machine learning and MLOps practices. Explore and implement innovative solutions to enhance the company’s machine learning capabilities.
This is a remote position.
This position is not eligible for sponsorship for work authorization now or in the future, including conversion to H1-B visa. Must be legally authorized to work in the country of employment without needing sponsorship for employment work visa status now or in the future.
Job duties include contact with other employees and access confidential and proprietary information and/or other items of value, and such access may be supervised or unsupervised. The Company therefore has determined that a review of criminal history is necessary to protect the business and its operations and reputation and is necessary to protect the safety of the Company’s staff, employees, and business relationships.
Education and Experience
Required:
- Bachelor's Degree in Computer Science or Computer Engineering or in lieu of a degree, at least 9 years of experience in software development
- 6+ years of experience in AI, ML, MLOps, and/or software development
- Highly proficient knowledge of software development life cycle (SDLC) methodologies and practices, particularly MLOps and DevOps
- Highly proficient knowledge of software development tools, platforms and languages, particularly cloud platforms
- Proven experience in developing, integrating, testing and deploying software solutions for cloud-based or internet-related tools
- Expert of relational databases and SQL
- Excellent written and verbal communication skills
- Proven experience in developing Microservices and in-depth knowledge of various Integration patterns
- Must be legally authorized to work in the United States without company sponsorship
- Must be at least eighteen (18) years of age
Preferred:
- Certified Software Development Professional (CSDP) or equivalent certification
- Experience in GenAI
- Ability to productionize applications externally-facing
Salary Context
This $127K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Sherwin-Williams, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($146K) sits 31% below the category median. Disclosed range: $127K to $165K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Sherwin-Williams AI Hiring
Sherwin-Williams has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Cleveland, OH, US. Compensation range: $140K - $165K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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