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About This Role
General Information
Req # WD00105539 Career area: Artificial Intelligence Country/Region: United States of America State: North Carolina City: Morrisville Date: Friday, October 16, 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
We are seeking a highly motivated AI Developer – New Graduate to join our US engineering team. This role is based in Morrisville, NC and operates under a hybrid policy. The target start date will be January 2027. This is an entry-level role designed for recent graduates with a strong software engineering foundation and hands-on exposure to AI/ML or Generative AI through internships, research, academic projects, or personal projects.
You will participate in the design and development of enterprise AI applications using technologies such as LLMs, RAG, AI Agents, tool/function calling, embedding/vector search, and AI workflow orchestration.
This is not a purely research-focused AI position. You will work on real enterprise products and collaborate with product managers, architects, developers, and business stakeholders throughout the solution lifecycle.
We are particularly interested in candidates with the potential to grow into an FDE-style engineer — someone who can combine strong technical execution with business understanding, customer communication, rapid problem solving, and end-to-end ownership.
Job Responsibilities
- Design, develop, test, and enhance enterprise AI applications and AI-enabled product capabilities.
- Build solutions involving LLMs, RAG, AI Agents, prompt engineering, tool/function calling, vector/embedding search, and AI workflow orchestration.
- Develop backend services, APIs, data integrations, and other components required to connect AI capabilities with enterprise systems.
- Participate in AI evaluation, testing, debugging, and performance improvement to continuously improve solution quality, reliability, latency, and user experience.
- Work with product managers, architects, business stakeholders, and engineering teams to understand business problems and translate requirements into practical technical solutions.
- Participate in requirement discussions, solution design, technical reviews, demos, UAT support, and production deployment.
- Prototype and evaluate new AI technologies and quickly determine their feasibility for real business scenarios.
- Troubleshoot technical and production issues and continuously improve application stability and engineering quality.
- Develop a strong understanding of business processes and user journeys rather than focusing only on individual technical tasks.
- Gradually take greater ownership of solutions from problem definition and prototyping through implementation and delivery.
- For candidates demonstrating strong technical and communication capabilities, gradually develop toward an FDE role, directly partnering with stakeholders to explore problems, prototype solutions, and drive AI use cases from concept to production.
Basic Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical field.
- Recent graduate or early-career candidate with strong academic, internship, research, or project experience.
- Strong programming fundamentals, with hands-on experience in Python.
- Hands-on project, research, or internship experience in at least one AI-related area such as:
- + Large Language Models (LLMs) + Generative AI applications + RAG + AI Agents + AI chatbot / conversational AI + NLP + Machine Learning + Embedding / vector search + Tool / Function Calling + AI automation
- Ability to clearly explain the architecture, implementation, challenges, and results of AI or software projects you have personally worked on.
- Good understanding of software engineering fundamentals, including APIs, databases, debugging, source control, and software development lifecycle.
- Strong learning ability and curiosity about new AI technologies.
- Strong problem-solving skills and willingness to work through ambiguous problems.
- Good verbal and written communication skills, with the ability to collaborate with both technical and non-technical stakeholders.
- Strong ownership mindset and willingness to learn business processes in addition to technology.
- Ability to work effectively in a global, cross-functional engineering environment.
Preferred Qualifications:
- Master’s degree in Computer Science, AI, Machine Learning, Data Science, or a related field.
- Internship or project experience building production-oriented LLM or Generative AI applications.
- Experience with frameworks or technologies such as LangChain, LangGraph, AutoGen, Hugging Face, PyTorch, or similar AI development frameworks.
- Experience with RAG pipelines, vector databases, embeddings, Agent workflows, or tool/function calling.
- Experience with backend development using Python frameworks such as FastAPI or Flask.
- Familiarity with relational databases, SQL, REST APIs, cloud platforms, Docker, or Kubernetes.
- Experience developing and evaluating AI prototypes and converting prototypes into usable applications.
- Exposure to LLM evaluation, prompt evaluation, test datasets, or AI observability is a plus.
- Experience working directly with users, customers, product teams, or business stakeholders through internships, university projects, startup experience, or other activities.
- Demonstrated ability to independently explore an unfamiliar problem, quickly build a prototype, collect feedback, and iterate on the solution.
- Interest and potential to grow toward a Forward Deployed Engineer (FDE) role combining engineering, product thinking, business understanding, and stakeholder engagement.
- Mandarin Chinese communication skills are a plus due to frequent collaboration with China-based engineering teams.
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
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 11,520 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 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Lenovo AI Hiring
Lenovo has 25 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% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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