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About This Role
Description:
Total Warehouse is seeking a digital growth leader who can turn TotalWarehouse.com into a powerful source of market authority, qualified demand, and measurable revenue growth.
This is not a traditional marketing manager role. It is an opportunity to build a business within the business and create a digital advantage that competitors will struggle to replicate.
Reporting directly to the CEO, the Director of Digital Growth & AI will own the strategy and operating system for website growth, SEO, AI-search visibility, competitive intelligence, content authority, conversion optimization, marketing automation, and digital demand generation.
The right leader is both strategic and hands-on. You will evaluate where we are winning and losing, set priorities, test ideas quickly, quantify results, and build the internal team and external resources needed to scale.
What Success Looks Like
- Total Warehouse becomes a trusted digital authority across material handling, fleet lifecycle management, asset management, warehouse automation, service, rentals, parts, batteries and lithium solutions, training, and related technologies.
- TotalWarehouse.com consistently generates more qualified opportunities and measurable pipeline for Sales.
- Our organic search presence, local visibility, AI-answer visibility, conversion rates, and competitive share of search improve quarter over quarter.
- AI-enabled workflows accelerate research, content development, analysis, testing, reporting, and execution without sacrificing accuracy or brand credibility.
- Leadership has a clear, reliable scorecard connecting digital investment to leads, opportunities, pipeline, revenue, and strategic market position.
- A scalable digital growth team, agency model, technology stack, and execution cadence are established.
Key Responsibilities
Digital Growth and Website Ownership
- Own TotalWarehouse.com as a measurable business asset and demand-generation platform.
- Create and execute a multi-year digital growth roadmap aligned with company revenue priorities and market opportunities.
- Build an ongoing test-and-improve program for organic traffic, engagement, user experience, conversion paths, lead quality, and revenue contribution.
- Identify high-value opportunities by product, service line, industry, customer problem, geography, and stage of the buying journey.
- Direct website architecture, landing pages, tools, calculators, comparison content, resource centers, and other digital experiences that help customers make decisions.
SEO, AI Search, and Authority Building
- Own technical SEO, on-page SEO, local SEO, content SEO, internal linking, site architecture, structured data, and authority-building initiatives.
- Develop topic clusters and high-value resources that demonstrate deep expertise and answer the questions customers ask before contacting Sales.
- Improve visibility across traditional search engines and AI-powered answer and discovery experiences.
- Establish standards for credible, accurate, expert-led content, including case studies, guides, videos, customer stories, comparison pages, and executive or subject-matter-expert insights.
- Monitor branded and non-branded visibility, competitor rankings, citations, referral sources, backlinks, content gaps, and emerging search behavior.
AI Enablement and Automation
- Use AI as a daily force multiplier for market research, competitive analysis, content operations, SEO analysis, data interpretation, personalization, workflow automation, and productivity.
- Build repeatable AI-enabled workflows with appropriate human review, source validation, brand controls, and quality standards.
- Evaluate and implement practical tools across the marketing technology stack, which may include platforms such as Microsoft Copilot, ChatGPT, Claude, Gemini, GA4, Google Search Console, Semrush or Ahrefs, Screaming Frog, HubSpot or comparable CRM/automation platforms, and business intelligence tools.
- Help the broader organization understand where AI can improve customer acquisition, knowledge sharing, speed, and decision quality.
Demand Generation and Conversion
- Build integrated digital programs that create qualified opportunities for Sales across Total Warehouse business lines.
- Improve conversion from visitor to lead, lead to qualified opportunity, and opportunity to revenue in partnership with Sales leadership.
- Create targeted campaigns and digital experiences around customer needs, market segments, territories, and strategic growth priorities.
- Ensure paid media, email, retargeting, marketing automation, and account-based initiatives support a clear commercial objective and measurable return.
Competitive Intelligence and Market Expansion
- Establish a repeatable competitive benchmarking program for websites, search visibility, content, offers, conversion experiences, digital tools, and market positioning.
- Turn competitive and customer insights into prioritized actions, experiments, and new growth opportunities.
- Identify areas where Total Warehouse can own a topic, customer problem, geographic market, or digital experience before competitors do.
- Present clear recommendations to the CEO and leadership team, including expected impact, required investment, risks, and measures of success.
Operating System, Team, and Accountability
- Build a scalable digital growth operating system with priorities, owners, timelines, review cadences, decision rights, quality standards, and measurable outcomes.
- Lead and develop internal team members while managing agencies, freelancers, technology partners, and subject-matter experts.
- Create weekly and monthly scorecards covering leading indicators and commercial outcomes.
- Control budgets and allocate resources based on opportunity, evidence, and return rather than activity volume.
- Partner closely with Sales, Operations, IT, business-unit leaders, and subject-matter experts to convert company knowledge into market authority and demand.
First-Year Priorities
First 90 Days
- Audit the website, analytics, CRM flow, content, SEO, competitors, technology stack, resources, and baseline performance.
- Deliver a prioritized growth roadmap and scorecard.
Months 4-6
- Launch high-impact technical, content, conversion, local-search, and AI-workflow improvements.
- Establish testing and reporting cadence with Sales and leadership.
Months 7-12
- Scale what works, expand authority across priority categories and markets.
- Strengthen attribution and present a forward investment plan tied to pipeline and revenue.
How Performance Will Be Measured
- Qualified organic and digital leads, sales-accepted opportunities, influenced pipeline, and attributable revenue.
- Growth in non-branded organic visibility, priority keyword/topic coverage, local visibility, AI-answer visibility, and competitive share of search.
- Website conversion rate, landing-page performance, lead quality, and progression through the sales funnel.
- Content authority, backlinks and citations, engagement with high-intent resources, and contribution by business line.
- Speed and
Requirements:
7+ years of progressive B2B digital marketing, growth, demand generation, SEO, or related
experience, including meaningful ownership of a company website or digital acquisition
channel.
- Demonstrated record of improving organic traffic, qualified leads, conversion, pipeline,
revenue, or competitive search position. Candidates must be able to explain the starting point,
actions taken, and measurable results.
- Hands-on experience with SEO strategy and execution, website analytics, conversion
optimization, content strategy, and marketing technology.
- Practical, current use of AI tools in recurring business workflows, not solely for basic
copywriting.
- Ability to translate business priorities into a clear digital roadmap, operating cadence, KPIs,
and accountable execution.
- Strong analytical and financial judgment, including the ability to connect marketing
investments to pipeline, revenue, customer value, and return on investment.
- Executive-level communication skills and comfort presenting recommendations, tradeoffs,
performance, and resource needs directly to senior leadership.
- Builder mentality: resourceful, curious, highly structured, willing to test, and comfortable
moving between strategy and hands-on execution.
Preferred Qualifications
- Experience in material handling, industrial equipment, manufacturing, industrial distribution,
logistics, warehouse automation, commercial services, industrial technology, or another
complex B2B market.
- Experience leading a website redesign, digital transformation, content migration,
CRM/marketing automation integration, or attribution initiative.
- Experience managing internal team members plus agencies, contractors, or specialized
partners.
- Working knowledge of structured data/schema, local SEO, technical site audits, content
clustering, backlink strategy, and AI-search or answer-engine optimization.
- Experience with multi-location, multi-product, or territory-based organizations and long,
consultative sales cycles.
Notice Regarding the Use of Artificial Intelligence in Application Review Notice of AI Use in Job Application Review
As part of our commitment in creating a fair, efficient, and consistent hiring process we may use artificial intelligence (AI) to help our recruiting teams organize, summarize, and analyze information provided by candidates, including resumes, application responses, and other materials submitted during the application process.AI may be used to identify patterns, highlight relevant skills, and experience, and assist in comparing a candidate’s qualifications with the requirement of a specific role. These tools are to improve efficiency and consistency while supporting more informed hiring decisions, which will ultimately be made by the hiring team.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Total Warehouse, 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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Total Warehouse AI Hiring
Total Warehouse has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Anaheim, CA, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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