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About This Role
Job Qualifications:
- College Level Degree or Equivalent.
- Experience operating a Powered Industrial Truck (PIT), hand truck, pallet jack, and other warehouse equipment.
- 5\-10 years of warehousing and experience preferred.
- 3\-5 years of leadership experience required.
- Strong verbal and written communication skills.
- Proficient computer skills.
Compensation/ Benefits
Certain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base salary range for this position is $84,040\.00 to 105,050\.00\. Actual salary will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. The range for this position in other geographic locations may differ. Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that is not included in the base salary.
The well\-being of WWT employees is essential. So, when it comes to our benefits package, WWT has one of the best. We offer the following benefits to all full\-time employees:
- Health and Wellbeing: Health, Dental, and Vision Care, Onsite Health Centers, Employee Assistance Program, Wellness program
- Financial Benefits: Competitive pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Tuition Reimbursement
- Paid Time Off: PTO \& Holidays, Parental Leave, Sick Leave, Military Leave, Bereavement
- Additional Perks: Nursing Mothers Benefits, Voluntary Legal, Pet Insurance, Employee Discount Program
Equal Opportunity Employer Minorities/Women/Veterans/Individuals with Disabilities
\#LI\-DP1
Requirements:
Warehouse Manager\-Shipping \- 3rd Shift (NAIC4\)
Why WWT?
At World Wide Technology, we work together to make a new world happen. Our important work benefits our clients and partners as much as it does our people and communities across the globe. WWT is dedicated to achieving its mission of creating a profitable growth company that is also a Great Place to Work for All. We achieve this through our world\-class culture, generous benefits and by delivering cutting\-edge technology solutions for our clients.
Founded in 1990, World Wide Technology (WWT) is a global technology solutions provider leading the AI and Digital Revolution. With more than $20 billion in annual revenue, WWT combines the power of strategy, execution and partnership to accelerate digital transformational outcomes for large public and private organizations. Through its Advanced Technology Center, a collaborative ecosystem of the world's most advanced hardware and software solutions, WWT helps clients and partners conceptualize, test and validate innovative technology solutions for the best business outcomes and then deploys them at scale through its global warehousing, distribution and integration capabilities.
With over 10,000 employees and more than 55 locations around the world, WWT's culture, built on a set of core values and established leadership philosophies, has been recognized 13 years in a row by Fortune and Great Place to Work® for its unique blend of determination, innovation and creating a great place to work for all. With this culture at its foundation, WWT bridges the gap between business and technology to make a new world happen for its customers, partners and communities.
Want to work with highly motivated individuals on high\-performance teams? Join WWT today!
Job Summary:
Warehouse Managers are responsible for overseeing and leading a group of 25\-75 warehouse personnel who work receiving, moving, picking, and shipping product throughout our warehouse and integration operations. Responsibilities include leading, developing, planning, and directing staff in support our department goals and objectives. The position reports to a Senior Warehouse Manager.
Job Responsibilities:
- Strategically create and improve organization's equipment operation best practices ensuring operators adhere to policy.
- Identify the interrelatedness of work activities across teams and recommend improvements to processes to increase effectiveness.
- Remove team barriers to implementing new ideas or approaches by securing the necessary resources or support from senior management.
- Ensure process improvement initiatives focus on measurable results, and help teams establish measures of success.
- Provide insight on ways to address short\-term production capacity constraints or issues (e.g., through increasing the number of workers or machines, increasing the number of shifts, sub\-contracting arrangements).
- Guide the receiving, shipping, and warehousing processes to ensure a consistent flow for production; implement new strategies and designs to improve the movement of materials.
- Conduct a deep review of data and issues to quickly reveal the root cause of problem.
- Recommend interim and long\-term solutions to complex problems to ensure successful resolution.
- Execute solutions to complex problems; guide the analysis of a problem all the way to a successful resolution.
- Encourage others to listen attentively and engage with the speaker by asking appropriate open\-ended questions; reflect on and reiterates key points and clarifies messages to help others reach agreement.
- Monitor non\-verbal reactions of others to identify and respond to areas of interest or concern.
- Ensure all viewpoints have been heard and discussed before reaching conclusions, offering guidance, or asserting own views.
- Prioritize workload and ensure that deadlines are successfully met; delegate or seek additional staff when necessary.
- Identify short\- and long\-term barriers to completing project plans and take corrective action to meet deliverable objectives.
- Manage multifaceted projects or assignments, such as projects that have several separate components, with minimal error and without missing important deliverables.
- Promote WWT safety guidelines.
- Drive teams to maintain a clean and organized (5S) warehouse operation.
Salary Context
This $84K-$105K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At World Wide Technology, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($94K) sits 43% below the category median. Disclosed range: $84K to $105K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
World Wide Technology AI Hiring
World Wide Technology has 13 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer, Data Scientist. Positions span Remote, US, New York, NY, US, Edwardsville, IL, US. Compensation range: $93K - $250K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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