Interested in this AI/ML Engineer role at World Wide Technology?
Apply Now →Skills & Technologies
About This Role
Qualifications:
- 15\+ years of progressive experience in enterprise technology sales, business development, and sales leadership, including significant time in a senior leadership role (Director, Senior Director, or VP) carrying or directly accountable for revenue, quota, or P\&L.
- Deep experience in technical consulting sales — selling complex, consultative, services\-led solutions (including infrastructure, cloud, data, AI, and modern application work) into large enterprise and service provider customers.
- Proven track record of building and scaling AI, cloud, or infrastructure\-led sales motions that delivered measurable, multi\-year revenue and pipeline growth.
- Strong, current relationships and demonstrated co\-selling experience with neoclouds (CoreWeave, Lambda, Crusoe, Nebius, or similar AI\-specific cloud providers).
- Executive presence and credibility at the C\-suite, CIO/CTO/CDAO/CISO, and board level — with a history of leading large, multi\-stakeholder pursuits and influencing multi\-year technology investments.
- Strong commercial instincts — pricing, deal structuring, contracting, partner economics, and the levers that turn strategic programs into repeatable revenue.
- This is a business development and sales leadership role, not a hands\-on technical role; the right candidate is technology\-fluent across AI infrastructure, GenAI, agentic AI, and data platforms, and able to translate the AI landscape into clear customer and partner outcomes — but is not expected to architect, configure, or build solutions personally.
- Demonstrated ability to lead through influence across sales, practices, partners, and executives in a complex, matrixed, global organization.
- Strong forecasting, pipeline management, and CRM discipline (Salesforce or equivalent); comfort operating in QBRs, pipeline reviews, and senior sales leadership cadences.
- Bachelor's degree or equivalent experience required; advanced degree (MBA or technical) preferred.
- Location flexible; proximity to a major WWT hub or AI customer/partner concentration preferred.
Travel Requirements
- 25–50%, including travel to customers, partners, executive events, and major WWT and industry conferences.
Certain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base pay range for this position is $200,000\-225,000 annually. 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 pay.
The well\-being of WWT employees is essential. 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 (Medical \& Prescription), Dental, and Vision Care, Onsite Health Centers (MO \& IL), Employee Assistance Program, Wellness program
- Financial Benefits: Competitive Pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Flexible Spending Accounts, Tuition Reimbursement
- Paid Time Off: PTO \& Holidays, Parental Leave, Medical Leave, Military Leave, Bereavement, Day of Caring
- Additional Perks: Family Planning Benefits, Nursing Mothers Benefits, Voluntary Legal, Voluntary Supplemental Accident/Illness/Hospital, Voluntary ID Theft, Pet Insurance, Employee Discount Program
Note: This is not an all\-encompassing list and should not be used as a complete description of the plan's benefits. For more information, see our US benefits website at wwt.com/us\-benefits.
We strive to create an environment where all employees are empowered to succeed based on their skills, performance, and dedication. Our goal is to cultivate a culture of belonging that encourages innovation, collaboration, and respect for all team members, ensuring that WWT remains a great place to work for all!
If you require accessibility accommodation(s) or adjustment during any stage of the hiring
process, please let your WWT Recruiter know. The recruiter will work with you to understand your needs and help ensure an accessible experience throughout the interview process.
If you have any questions or concerns about this posting, please email [email protected]
World Wide Technology is an Equal Opportunity Employer.
\#LI\-NO1
Requirements:
Why WWT?
World Wide Technology (WWT) strives to make a new world happen. WWT's work benefits clients and partners as much as it does its people and community across the globe.
Founded in 1990, WWT brings together strategy, deep technical expertise and world\-class
partnerships to help public and private sector organizations design, build and scale intelligent AI, digital, cybersecurity, cloud and infrastructure solutions. Through its Advanced Technology Center (ATC)—a collaborative ecosystem featuring state\-of\-the\-art hardware and software—WWT enables clients and partners to conceptualize, test and validate innovative technology and then deploy solutions at scale using its global integration and distribution capabilities.
With more than 14,000 team members and over 60 locations globally, WWT's culture—grounded in core values and leadership philosophies—has been recognized by Fortune® and Great Place to Work for its commitment to innovation, trust and creating a great place to work for all. WWT provides products and services to large enterprise, global service provider and public sector clients in up to 130 countries across six continents. Softchoice, a World Wide Technology company, supports commercial and SMB markets in the U.S. and Canada.
Want to work with highly motivated individuals on high\-performance teams? Join WWT today!
Why join WWT as the AI Sales Leader – VP?
This is a senior, revenue\-leading role accountable for shaping and scaling WWT's AI business across the Global Service Provider portfolio. The AI Sales Leader – VP will own the strategy, growth, and execution of AI sales — partnering with WWT's field organization, executive leadership, neocloud and AI/silicon partners, and the broader AI ecosystem to capture the most strategic AI opportunities in the market. This is a business development and sales leadership role, not a hands\-on technical role; the right candidate is an enterprise seller and executive operator who is technology\-fluent and consultative, with the experience and credibility to drive multi\-million\-dollar AI initiatives at the C\-suite and board level.
What will you be doing?
As the AI Sales Leader – VP, you will define WWT's AI sales strategy across the GSP business, build and execute the go\-to\-market motions that drive AI bookings and pipeline, and serve as the senior executive face of WWT's AI portfolio to our most important customers and partners. You will lead through influence across Client Executives, Account Managers, technical practices, marketing, and the partner ecosystem — including neoclouds (e.g., CoreWeave, Lambda, Crusoe, Nebius). You will operate at the intersection of strategy, sales, and partner co\-investment, converting WWT's AI capabilities — AI\-ready infrastructure, data platforms, GenAI, agentic AI, and AI\-driven services — into measurable, repeatable revenue motions.
Responsibilities:
- Own the AI sales strategy, plan, and revenue targets for the GSP business — including bookings, pipeline, conversion, and growth across AI\-ready infrastructure, data platforms, GenAI, and AI services.
- Lead the go\-to\-market motion for AI across GSP — defining priority customer segments, plays, messaging, and pursuit strategies, and translating them into repeatable, measurable selling motions executed by the field.
- Serve as the senior executive sponsor on the most strategic AI opportunities — partnering with Client Executives, Account Managers, and SEs to qualify, advance, and close multi\-million\-dollar deals across discovery, executive engagement, value justification, proposal, negotiation, and close.
- Build and own executive\-level relationships with neoclouds (CoreWeave, Lambda, Crusoe, Nebius, and other emerging AI\-cloud providers) — driving joint pipeline, co\-selling motions, joint solutions, and co\-investment programs.
- Represent WWT at the C\-suite, CIO/CTO/CDAO/CISO, and board level across our most strategic customers — leading executive briefings, business value reviews, and multi\-year AI transformation conversations.
- Partner with WWT's technical practices, consulting teams, and Advanced Technology Center (ATC) to translate AI capabilities into commercial offers and to ensure that strategic pursuits are supported with the right architects, advisors, and proof points.
- Drive consistent sales discipline across the AI business — forecast accuracy, pipeline hygiene, deal qualification, and program ROI — and present cadence\-level updates to senior WWT leadership through QBRs, pipeline reviews, and executive operating reviews.
- Influence WWT's broader AI strategy — including offer portfolio, pricing, partner strategy, and investment priorities — based on customer, partner, and competitive signal from the field.
- Build and scale a high\-performing AI sales motion across the GSP organization — coaching field sellers, enabling account teams, and mentoring the next generation of AI sales talent at WWT.
- Represent WWT externally as a senior voice in the AI market — through customer events, partner events, analyst engagements, and select industry forums.
Salary Context
This $200K-$225K 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $200K to $225K.
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.
World Wide Technology AI Hiring
World Wide Technology has 31 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Remote, US, Hartford, CT, US, St. Louis, MO, US. Compensation range: $104K - $300K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.