Interested in this AI/ML Engineer role at World Wide Technology?
Apply Now →About This Role
- Computer Vision: Deep, hands\-on experience across classical computer vision, deep learning, and vision language model approaches, applied to real business problems. Comfort with VLM based methods is expected.
- Video Intelligence: Experience applying video understanding and analytics to real business problems, detecting events, activities, and anomalies across streaming and recorded video for use cases such as safety, quality, and throughput.
- Spatial Intelligence: Understanding of 3D scene understanding and spatial reasoning about physical environments, including how objects, people, and machines occupy and move through space. Connects computer vision to robotics and digital twin work.
- Sales and Commercial Understanding: Understands how the sales cycle works, how opportunities qualify and progress, and how a technical solution connects to a customer's business case and to WWT's commercial outcomes. Able to partner with sellers as a business peer, not only as a technical resource.
- Business Thinking and Practice Development: Able to build a business case, prioritize opportunities, and think through how a capability or practice should evolve over time. Business minded and structured when approaching ambiguous problems, and able to contribute a point of view on the direction of the practice without needing to lead it.
- Robotics: Working knowledge strong enough to scope solutions, assess technologies objectively, and hold a credible robotics conversation with a customer independently. Prior hands\-on demonstration or proof of concept experience preferred.
- Customer Focus: Strong customer engagement and communication skills, with the ability to translate complex technical concepts for non\-technical business audiences and represent WWT credibly in the room.
- Sales Acumen: Proven ability to engage customers, understand their needs, and communicate the value of technical solutions.
- Autonomy and Range: Comfortable operating in a developing practice with ambiguity, building content, demonstrations, and thought leadership from the ground up.
Preferred:
- Digital Twin and Simulation: Working familiarity with digital twin and simulation environments such as NVIDIA Omniverse or Isaac, Unity, or Unreal, sufficient to collaborate on engagements where these areas converge.
- AI Assisted Demo Development: Experience building demonstrations and prototypes with modern AI assisted development tools, consistent with how the team builds and iterates today.
Education: Bachelor's degree in Computer Science, Robotics, Engineering, or a related field.
Certifications or Advanced Degrees: Relevant AI, computer vision, or robotics certifications preferred.
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 $151,000\.00 to $190,000\.00 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.
World Wide Technology is an Equal Opportunity Employer.
\#LI\-DP2
\#LI\-Remote
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!
What will you be doing?
We are seeking a skilled and dynamic Physical AI architect to join our pre\-sales team. The Technical Solutions Architect focused on Physical AI will help enable WWT sales teams and articulate the WWT Physical AI message to customers, both in person and virtually. This role extends computer vision and robotics capability on the AI \& Data Solutions team, helping customers understand how Physical AI technologies can address their business needs and helping them design and deploy solutions that fit their environment. Computer vision is the primary focus of the role, with robotics as a growing and essential part of the practice. The TSA\-Physical AI role expands WWT's ability to support customer Physical AI requests and initiatives, and reports to the AI \& Data Solutions Practice Manager within the WWT Global Solutions \& Architecture team.
Responsibilities:
- Sales Enablement: Help WWT sales teams identify, position, and sell Physical AI solutions, strengthening their ability to carry the conversation with customers.
- Customer Engagement: Partner consultatively with customers to understand their needs, challenges, and goals, and help them design and deploy Physical AI solutions tailored to their environment.
- Computer Vision Solutioning: Lead computer vision opportunities across the full modern toolkit, including classical computer vision, deep learning, video intelligence, spatial intelligence, and vision language model approaches, translating business problems into practical solutions.
- Robotics Solutioning: Scope and architect robotics solutions, assess available technologies objectively, and hold technically credible robotics conversations with customers.
- Opportunity Qualification: Utilize qualification tools and concepts to identify and qualify service opportunities and gather technical requirements, ensuring alignment with customer needs and business goals.
- Proof of Concept Development: Design and execute demonstrations and proofs of concept in the WWT ATC and AI Proving Ground that show real world Physical AI applications.
- Solution Design: Collaborate with internal teams to architect comprehensive Physical AI solutions that align with customer needs and business objectives.
- Partner Collaboration: Work with strategic partners to bring the right technologies into WWT solutions.
- Content Creation: Create research articles and technical content on Physical AI, computer vision, and robotics that build WWT's presence and enable the field.
- Knowledge Transfer \& Collaboration: Facilitate knowledge sharing across the team and among AI sales specialists to foster a unified approach to Physical AI solutions.
- Business Case Development: Build the business justification for proposed solutions, connecting Physical AI capabilities to measurable customer outcomes such as cost, quality, throughput, and risk.
- Practice Development: Contribute to how the Physical AI practice evolves by identifying repeatable use cases, potential offerings, and market trends that inform where the practice should focus and invest.
Salary Context
This $151K-$190K 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 in Demand for This Role
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 ($170K) sits 21% below the category median. Disclosed range: $151K to $190K.
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.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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