Interested in this AI/ML Engineer role at Lenovo?
Apply Now →About This Role
### General Information
Req \#
WD00100958
Career area:
Services
Country/Region:
United States of America
State:
North Carolina
City:
Morrisville
Date:
Wednesday, June 17, 2026
Working time:
Full\-time
Additional Locations:
- United States of America \- North Carolina \- Morrisville
### Why Work at Lenovo
We are Lenovo. We do what we say. We own what we do. We WOW our customers.
Lenovo is a US$83 billion revenue global technology powerhouse, ranked \#196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full\-stack portfolio of AI\-enabled, AI\-ready, and AI\-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world\-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992\) (ADR: LNVGY).
This transformation together with Lenovo’s world\-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit www.lenovo.com, and read about the latest news via our StoryHub.
### Description and Requirements
Role Overview
The Regional Director, Hybrid Cloud \& AI Pre\-Sales leads Lenovo’s pre\-sales organization for Hybrid Cloud and AI solutions within their assigned region. This role is responsible for executing global pre\-sales strategy at the regional level, leading a team of solution architects and forward\-deployed engineers (FDEs), and ensuring high\-quality technical engagement with enterprise customers.
The Regional Director partners closely with regional sales leadership, solution leaders, and delivery teams to drive pipeline conversion, improve win rates, and position Lenovo as a trusted Hybrid Cloud \& AI partner in the market.
Key Responsibilities
Regional Pre\-Sales Leadership
- Lead and manage a regional pre\-sales organization of professionals, including solution architects and forward\-deployed engineers.
- Translate global Hybrid Cloud \& AI pre\-sales strategy into effective regional execution.
- Ensure consistent technical standards, engagement models, and customer experience across the region.
Sales Partnership \& Deal Execution
- Partner closely with regional sales leaders to support pipeline development, deal shaping, and opportunity prioritization.
- Provide hands\-on leadership for complex or strategic customer opportunities, ensuring strong technical credibility and value\-based solution design.
- Support proposal development, solution architecture, and executive\-level customer presentations.
Customer Engagement
- Act as a senior technical advisor for enterprise customers, aligning Hybrid Cloud \& AI solutions to business outcomes.
- Participate in key customer meetings to influence solution direction, address technical concerns, and support deal closure.
- Build trusted relationships with customer IT and business leaders across the region.
Talent Development \& Enablement
- Recruit, develop, and retain high\-performing pre\-sales and FDE talent within the region.
- Drive continuous skills development in AI, cloud, infrastructure, and solution consulting.
- Coach and mentor team members to strengthen technical depth, customer engagement, and commercial awareness.
Operational Excellence
- Ensure disciplined execution of pre\-sales processes, including pipeline qualification, proposal quality, and engagement governance.
- Track and report on key performance metrics such as win rates, presales effectiveness, and customer satisfaction.
- Collaborate with global and regional peers to share best practices and drive continuous improvement.
Scope \& Leadership
- Direct leadership of \~14 pre\-sales professionals within a defined geographic region.
- Close alignment with Regional Sales VPs and Solution Leaders.
- Focused on regional execution, customer impact, and team performance
\<\>·Required Qualifications
- Bachelor’s degree required; advanced degree preferred.
- 10–15 years of experience in pre\-sales, solution engineering, technical consulting, or technology services.
Preferred Qualifications.* Strong background in Hybrid Cloud, AI, infrastructure, and enterprise solution architectures.
- Proven experience supporting enterprise\-scale sales engagements and complex deals.
- Prior people leadership experience within technical or consulting teams.
- Experience operating in a regional or matrixed organization.
Key Capabilities \& Attributes
- Strong technical credibility combined with customer\-facing and commercial skills.
- Ability to translate complex technologies into clear customer value propositions.
- Effective coach and people leader with a focus on team development and performance.
- Strong communication and stakeholder management skills.
- Comfortable operating in fast\-moving, growth\-oriented technology environments.
Why This Role Matters
The Regional Director plays a critical role in bringing Lenovo’s Hybrid Cloud \& AI strategy to life in the field. By leading a high\-performing regional pre\-sales team, this role directly impacts customer success, revenue growth, and Lenovo’s reputation as a trusted solutions provider. The Regional Director serves as the key link between global strategy and field execution, ensuring customers receive technically sound, business\-aligned solutions at scale.
Hybrid Work Model:
For candidates based near our Raleigh, NC or Chicago, IL offices, we offer a flexible hybrid work model designed to balance collaboration and focus—spending three days in the office with your team and two days working remotely each week.
The base salary budgeted range for this position is 220K \- 295K. Individuals may also be considered for bonus and/or commission. Lenovo’s various benefits can be found on www.lenovobenefits.com.
In compliance with Colorado's EPEWA, the expected application deadline for this position is August 1, 2026\. This applies to both external and internal candidates.
*We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.*
Additional Locations:
- United States of America \- North Carolina \- Morrisville
- United States of America
- United States of America \- North Carolina
- United States of America \- North Carolina \- Morrisville
Salary Context
This $220K-$295K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Lenovo, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($257K) sits 39% above the category median. Disclosed range: $220K to $295K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Lenovo AI Hiring
Lenovo has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Morrisville, NC, US. Compensation range: $170K - $295K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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.