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About This Role
QUALIFICATIONS:
- Experience selling to high\-growth, AI\-native or AI\-first software companies — including GenAI, causal AI, and AI scaleups — and comfort operating in venture\-backed, founder\-led environments alongside more traditional Fortune 1000 motions.
- Familiarity with the AI ecosystem and the specific infrastructure and platform decisions facing AI\-native customers, including model training and inference, GPU and accelerator economics, hybrid and neocloud deployment, and the build\-vs\-buy trade\-offs that come with rapid growth.
- 7–10 years of progressive sales experience in a relevant technology industry (infrastructure, cloud, AI/data, networking, security, or services\-led selling), with a consistent track record of pipeline ownership and quota attainment.
- Strong cultural fit with WWT's core values and a clear passion for continuous learning — pursuing certifications, engaging with ATC resources, and applying feedback quickly.
- Proven ability to develop new customer relationships from a standing start, multi\-thread across business and technical stakeholders, and position technical solutions in terms of business outcomes.
- Experience leading account planning with an extended team, calling at multiple levels of the customer organization (practitioner through C\-suite), and managing partner relationships.
- Outstanding communication, executive presentation, and organizational skills, including disciplined CRM and pipeline hygiene (Salesforce or equivalent).
- Experience selling into Fortune 1000 companies strongly preferred, including familiarity with long enterprise sales cycles and formal RFI / RFP / RFQ pursuits.
- Bachelor's degree or equivalent industry experience preferred; relevant OEM or technology certifications are a plus.
Want to learn more about Global Enterprise Sales? Check out the Solutions and Services we provide on the platform: https://wwt.com
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
- 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 $140,000\-160,000 \+ commission. Actual salary will be based on a variety of factors, including 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.
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!
Equal Opportunity Employer
\#LI\-NO1
Requirements:
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.
WWT was founded in 1990 in St. Louis, Missouri. We employ more than 12,000 people across WWT and Softchoice and generate more than $20 billion in annual revenue. We have an inclusive culture and believe our core values are the key to company and employee success. WWT is proud to have been included on the FORTUNE “100 Best Places to Work For®” list 15 years in a row!
Want to work with highly motivated individuals on high\-performance teams? Join WWT today!
RESPONSIBILITIES:
- Engage high\-growth, AI\-native customers — GenAI, causal AI, and AI application companies — adapting WWT's motion to their founder\- and CTO\-led buying patterns, faster sales cycles, and heavy AI infrastructure footprints.
- Translate complex AI and data platform needs (GPU compute, model serving, data pipelines, causal/agentic AI workloads) into deployable WWT solutions and services that scale with the customer's growth trajectory.
- Operate as a consultative seller — leading with discovery, framing recommendations around customer outcomes, and following through on commitments to build trust internally and externally.
- Help orchestrate complex customer relationships by leveraging WWT's High Performance Team (sales engineers, consulting architects, services, and partner managers) to qualify, advance, and close opportunities tied to account\-level bookings and pipeline goals.
- Build and maintain working relationships with Partner / OEM technical specialists, account managers, and business unit leaders to drive joint account plans, co\-selling motions, and joint pursuits.
- Identify and engage the right partners for each opportunity based on technical fit, customer preference, OEM program alignment, and deal economics — and lead joint briefings, demos, and proposal development as needed.
- Actively leverage senior team members and forums (deal reviews, account reviews, win/loss debriefs) to accelerate learning and apply WWT best practices.
Salary Context
This $140K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($150K) sits 30% below the category median. Disclosed range: $140K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
World Wide Technology AI Hiring
World Wide Technology has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Remote, US, San Francisco, CA, US, New York, NY, US. Compensation range: $80K - $235K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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