Interested in this AI/ML Engineer role at Lenovo?
Apply Now →About This Role
General Information
Req # WD00106765 Career area: Services Country/Region: United States of America State: North Carolina City: Morrisville Date: Tuesday, October 13, 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 #153 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
Lenovo is seeking a global services leader to build, operate and scale the Power and Cooling support capability within AI Data Center Services (AIDCS). The role owns service delivery for mission-critical AI and HPC environments, including global workforce planning, team growth, technical escalation governance and portfolio P&L. This is a support-management role and does not own facilities operations, planned plant maintenance or hands-on engineering execution.
Key Accountabilities
Global Support Operations
- Own the global, follow-the-sun support model, including remote and field response, escalation, on-call coverage and OEM or partner integration.
- Manage SLAs, KPIs, major incidents, root-cause actions and executive customer reviews.
- Establish consistent incident, problem, change, knowledge and service-improvement processes.
- Approve the support readiness and transition of new deployments into steady-state service.
Global Workforce Leadership
- Forecast demand and own global scheduling for incidents, escalations, commissioning support, change windows and specialist interventions.
- Balance priorities and contingency coverage across customers, geographies and time zones.
- Manage skills coverage, utilization, productivity, travel, contractor use and on-call capacity against service and financial targets.
- Resolve resource gaps through recruitment, development, partners, contractors or OEM support, supported by accurate capacity reporting.
Team Growth and Leadership
- Build, lead and scale a geographically distributed team of power, electrical, mechanical and liquid-cooling specialists.
- Own organisation design, recruitment, performance, development, succession and retention.
- Create competency frameworks and career paths, while setting the right mix of employees, contractors, partners and OEM resources.
- Develop regional leaders and foster an accountable, inclusive and customer-focused culture.
P&L, Commercial and Growth Ownership
- Own the support portfolio P&L, annual plan, budget and forecast, with accountability for revenue, margin, operating cost and profitability.
- Control labor, travel, subcontractor, spares and tooling costs while protecting service commitments.
- Own service costing, pricing and commercial governance, and review bids and contracts for delivery and financial risk.
- Grow recurring revenue through standard offerings, service attach, renewals, lifecycle services and portfolio expansion.
Technical Support, Safety and Lifecycle Governance
- Act as the senior AIDCS escalation and support authority across liquid cooling and critical power infrastructure, including CDU systems, UPS, switchgear, generators and rack power.
- Provide technical governance for CDU operation and support, including coolant flow, pressure, temperature, filtration, controls, alarms and integration with facility and technology cooling loops.
- Understand water and coolant chemistry requirements, including water quality, corrosion control, conductivity, pH, biological growth, contamination, sampling and treatment, and coordinate specialist analysis and corrective action.
- Govern supportability, resilience, production acceptance, service procedures, spares, warranties and vendor performance; facilities teams or approved service providers remain accountable for physical maintenance and repair.
- Ensure support activities comply with electrical, mechanical, pressure-system, coolant, environmental and data-center safety requirements.
- Own support risk, escalation readiness, business continuity and emergency-response coordination.
Basic Qualifications
- 10+ years in mission-critical data-center, power, cooling or technical support services, including five years leading multi-region operations.
- Demonstrable services P&L ownership covering revenue, margin, budgeting, forecasting, investment and cost control.
Preferred Qualifications
- Experience leading 24×7 support, global resource scheduling and major incidents across complex customer environments.
- Strong working knowledge of electrical, mechanical and liquid-cooling infrastructure, including CDUs and water or coolant chemistry, sufficient to govern support, interpret operating conditions and lead escalations without being the hands-on engineer.
- Experience commercializing support services, managing bids or contracts and growing recurring revenue.
- Proven success building and scaling a geographically distributed technical support team.
- Degree or equivalent experience in electrical, mechanical, data-centre or related engineering.
- Relevant ITIL, service management, engineering or data-centre certification.
- Experience with advanced liquid-cooling technologies and project accounting, service pricing or contract management.
- Strong executive communication, commercial judgement and stakeholder leadership;
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
PAY TRANSPARENCY
The anticipated annual compensation range for this position is 139,800–214,360 USD. Final compensation will be based on relevant experience, skills, and business considerations. Individuals may also be considered for bonuses and/or commissions. Lenovo’s various benefits can be found at www.lenovobenefits.com In compliance with Colorado’s Equal Pay for Equal Work Act (EPEWA), the expected application deadline for this position is 12-31-2026. This requirement applies to both internal and external candidates.
Salary Context
This $139K-$214K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($177K) sits 16% below the category median. Disclosed range: $139K to $214K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Lenovo AI Hiring
Lenovo has 21 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Architect. Positions span Morrisville, NC, US, Petaling Jaya, Selangor, Malaysia, North Carolina, United States. Compensation range: $202K - $289K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.