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About This Role
Regional Sales Trainer (RST)
The Regional Sales Trainer in the retail company will provide comprehensive training support in the areas of sales, service, and customer experience. The Regional Sales Trainer will be a new initiative and change promoter. When not delivering training experiences the RST will travel to Home Centers providing coaching, remedial training, and motivation to home center teams. The Regional Sales Trainer will report to the Director of Sales with a dotted line approach to the Regional Vice President. The RST will assist the Regional Vice President to increase revenue by educating, training, and streamlining Sales Professionals understanding of the home buying and sales process.
Regional Sales Trainer (RST) Responsibilities:
- Strategy/ Business Partner: The RST will partner with the RVP to help determine strategic business goals for the region
- Assist RVP in the identification of Power Pack/ under-performing Home Centers
- Assist in the development and execution of recovery strategies for under-performing Home Centers and Sales Professionals
- SALES: Schedules and executes all enterprise-wide sales training experiences (i.e., Sales Essentials, Next Level Sales Essentials, Pro Series, and Customer Experience)
- Works with Zone Assistant as well as Retail Learning and Performance team to ensure all logistical, collateral, and material needs are ready to deliver a world-class learning experience to participants
- The RST will take a lead role in evaluating TMX and implementing process change with TMX in mind
- Participates in “train-the-trainer” events and serves as lead trainer for all systems training related to enterprise sales management (i.e., Vantage/ follow up)
- When not delivering training, the RST will travel to Home Centers spending time coaching and remediating the sales process, Sales Essentials, Next Level Sales Essentials, and systems training
- Provide support to Home Centers in region who are struggling to generate business and move prospects through the sales process
- Provide coaching and training to the Sales Manager group
- Provide corporate and regional Promo support
- Ensures any product or marketing initiative from the home office is translated to the sales team such as Tuff Shed/ garages, Red Tag, etc. These initiatives are designed to provide for the needs of our customers and ultimately increase sales
- Works with the RVP to identify training and coaching opportunities as it relates to YES to Fund process
- Practice and implement up to date terminology in the Sales Essentials Flipping book
- Assist the RVP in developing and facilitating regional sales incentives and contests
- OTHER: Provides ad-hoc training based on new sales, marketing or finance programs or other unique needs specific to region
- Additional emphasis/ focus on Power Pack home centers in their respective region
- Deliver CX training as well as being the voice champion of CX for the region.
- Connected to the voice of our customers by partnering with the CX department and translating that voice into actionable changes benefiting the customer experience delivered by the sales team
- Remedial training as necessary on various retail sales and sales management practices
- Home Center visits to provide overall support and motivation to sales teams
- Determine how best to communicate with the RVP on issues that might be presented to the RST
- Provide learning and training to the sales team on features/ products under the Clayton Built brand of products
Compensation:
- As a Regional Sales Trainer with Clayton, you will receive a base salary ranging from $105,000 - $140,000 per year dependent upon experience.
Why Clayton?
Full-time team members have the flexibility to create their own health, dental, and vision benefits package. Clayton provides competitive 401K programs including investment options and company matching for full and part time team members after one year to help our team members achieve their financial goals. Additional benefits include paid parental leave, Employee Assistance Programs, paid time off, paid holidays and more.
As part of Clayton’s commitment to Opening Doors to a Better Life, Clayton is now providing paid time for Team Members to volunteer to causes that are meaningful to them through the Clayton Impact program.
At Clayton, we encourage holistic wellness with physical, nutritional, social, financial, spiritual and occupational programs.
Home Centers are closed on Sundays – we believe in offering a balanced working environment.
Clayton is committed to creating an inclusive workplace. Clayton is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Business Unit - B00009
Clayton Retail
Salary Context
This $105K-$140K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 13,154 AI roles we're tracking, AI/ML Engineer positions make up 90% of the market. At Clayton, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($122K) sits 20% below the category median. Disclosed range: $105K to $140K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Clayton AI Hiring
Clayton has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Maryville, TN, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 11% (1,409 positions) offer remote work, while 11,687 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 13,154 open positions tracked in our dataset. By seniority: 996 entry-level, 8,260 mid-level, 2,510 senior, and 1,388 leadership roles (Director, VP, C-Level). Remote roles make up 11% of the market (1,409 positions). The remaining 11,687 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 13,154 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (11,857), AI Software Engineer (350), AI Product Manager (327). 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 (996) are outnumbered by mid-level (8,260) and senior (2,510) 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 1,388 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 11% of all AI roles (1,409 positions), with 11,687 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (2,269 postings), Aws (1,390 postings), Azure (1,072 postings), Rag (923 postings), Gcp (833 postings), Pytorch (716 postings), Prompt Engineering (653 postings), Tensorflow (594 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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