Interested in this AI/ML Engineer role at World Wide Technology?
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About This Role
Required Qualifications:
- Minimum8 years in software engineering, developer tools sales, application platform sales, or developer relations/advocacy with a transition to commercial or client\-facing roles.
- Deep understanding of modern enterprise development practices: CI/CD, DevOps, platform engineering, cloud\-native development, and AI\-assisted coding workflows.
- Working knowledge of NVIDIA's developer tools and libraries: CUDA toolkit, cuDNN, TensorRT, RAPIDS, Triton, NeMo, AI Workbench. Comfortable co\-selling alongside NVIDIA teams.
- Comprehensive knowledge of the enterprise coding assistant market: GitHub Copilot, Amazon Q Developer, Gemini Code Assist, Cursor, Claude Code, Windsurfand Devin, and emerging tools. Understands licensing models, enterprise deployment patterns, security/IP considerations, and productivity measurement.
- Able to translate developer productivity tools into business\-outcome conversations: velocity improvements, defect reduction, developer retention, time\-to\-market acceleration.
- Comfortable scoping and proposing consulting engagements: application acceleration assessments, coding assistant pilots, developer productivity benchmarking.
- Bachelor's degree in computer science, software engineering, or related field required.
Preferred Qualifications:
- Prior experience at NVIDIA, a developer tools company (GitHub, JetBrains, Atlassian), or a cloud platform provider (AWS, Azure, GCP) in a developer relations, developer advocacy, or developer\-focused sales role.
- Existing relationships with enterprise development leadership (CTOs, VP Engineering) across Fortune 500 accounts.
- Experience with NVIDIA CUDA certification or equivalent GPU computing credentials.
- Background in AI/ML application development, not just infrastructure.
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 $150,000\.00 to $180,000\.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. 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
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 have any questions or concerns about this posting, please email [email protected].
\#LI\-MG2
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.
Founded in 1990, WWT is a global technology solutions provider leading the AI and Digital Revolution. WWT combines the power of strategy, execution and partnership to accelerate digital transformational outcomes for organizations around the globe. 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 12,000 employees across WWT and Softchoice and more than 60 locations around the world, WWT's culture, built on a set of core values and established leadership philosophies, has been recognized 15 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.
Want to work with highly motivated individuals on high\-performance teams? Join WWT today!
What will you be doing?
The AI Development Specialist is a dedicated developer\-facing engagement resource focused on two related sales motions: (1\) selling coding assistant \& AI Native Engineering (AINE) solutions and related services to enterprise development organizations, (2\) co\-selling with NVIDIA teams on accelerating enterprise applications through NVIDIA\-optimized libraries, SDKs, and development tools.
AI Development Specialists are assigned to each Global Enterprise Segment division and function as shared resources across the regional PODs within that division. This role engages directly with enterprise customers' development and engineering teams—CTOs, VP Engineering, platform engineering leads, and developer experience teams—to position WWT as a partner for developer productivity and AI\-accelerated application development.
This is a specialized role that combines deep knowledge of the NVIDIA developer platform, comprehensive understanding of the enterprise coding assistant market, and consultative selling skills that translate developer productivity tools into business\-outcome conversations. The role requires the ability to co\-sell alongside NVIDIA and AINE partner teams, develop repeatable service packages, and work within the POD structure to connect application\-layer acceleration to infrastructure and services decisions.
Responsibilities:
Coding Assistant \& AINE Solutions with Services
- Position and sell coding assistant \& AINE solutions: vendor evaluation (GitHub Copilot, Amazon Q Developer, Google Gemini Code Assist, Cursor, Claude Code, Windsurfand Devin), pilot design, adoption strategy, developer workflow integration, security/governance for AI\-generated code, and productivity measurement.
- Work in close coordination with Global Solutions \& Architecture and Service Consulting \& Engineering Develop repeatable service packages: coding assistant pilot programs, developer productivity benchmarking, adoption and change management, and ROI measurement.
- Engage enterprise development teams on the full spectrum of AI\-assisted development: code generation, code review, testing automation, documentation, and intelligent IDE integration.
NVIDIA Developer Platform Co\-Sell
- Co\-sell with NVIDIA teams on accelerating enterprise applications through NVIDIA\-optimized libraries (CUDA, cuDNN, TensorRT, RAPIDS, Triton Inference Server), SDKs, and development frameworks.
- Translate NVIDIA's developer platform value proposition into customer\-specific application acceleration opportunities—identifying workloads where library optimization drives measurable performance improvement.
- Collaborate with NVIDIA\-embedded SE resources on joint customer engagements, technical demonstrations, and proof\-of\-concept designs that showcase application acceleration.
- Stay current on NVIDIA's developer tools roadmap: CUDA toolkit, AI Workbench, NeMo, Omniverse SDK, and emerging frameworks.
Enterprise Developer Engagement
- Engage directly with enterprise customers' development and engineering leadership—CTOs, VP Engineering, platform engineering leads, developer experience teams—to position WWT as a developer productivity and AI\-accelerated application development partner.
- Develop application acceleration assessments that identify workloads suitable for NVIDIA library optimization and quantify the performance and cost impact of acceleration.
- Contribute developer\-ecosystem intelligence to the broader AI team: emerging tools, competitive dynamics, developer community trends, and customer adoption patterns.
POD \& Cross\-Functional Collaboration
- Operate as an integrated member of the three\-person regional POD (AI Solutions Executive, AI Solutions Architect, AI Services Consultant), participating in weekly POD cadence, joint pipeline reviews, and account planning.
- Operate as a trusted AI subject matter resource to field sales teams, embedding into account planning and strategy sessions to surface workforce AIand coding assistant (AINE)
- Work alongside PODAI Solutions Architects where application\-layer acceleration connects to infrastructure decisions—e.g., where library optimization drives GPU compute sizing, inference architecture, or AI Factory configuration.
- Collaborate with AI Services Consultants on engagements where coding assistant and workforce AI services are part of a broader consulting proposal.
- Develop strategy and drive customer engagement with AI Solutions Executive to identify, qualify, and close business linked to development pursuits.
- Participate in division\-level pipeline reviews, contributing developer\-facing opportunity intelligence to the AI Solutions Director's operating cadence.
- Integrate with the operating rhythm and culture of the Regional sales teams to work in collaboration to set strategy and co\-sell AI solutions across the territory.
Salary Context
This $150K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($165K) sits 9% below the category median. Disclosed range: $150K to $180K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
World Wide Technology AI Hiring
World Wide Technology has 6 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer. Positions span Remote, US, St. Louis, MO, US, Seattle, WA, US. Compensation range: $150K - $235K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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