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About This Role
### General Information
Req \#
WD00102643
Career area:
Hardware Engineering
Country/Region:
United States of America
State:
California
City:
San Jose
Date:
Friday, July 24, 2026
Working time:
Full\-time
Additional Locations:
- United States of America \- California \- San Jose
### 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 Principal Architect, AI Developer Productivity to fundamentally accelerate the velocity and output of LATC’s AI engineering teams. This is a senior individual contributor role with organization\-wide architectural authority and influence—designed for someone who has led large\-scale developer productivity or engineering platform efforts and knows how to translate that experience into measurable gains for a team of AI researchers and engineers.
You will design and drive the automation of developer workflows from end to end—embedding AI tooling, agentic systems, and intelligent automation at every stage of how LATC’s engineers plan, build, test, and ship. You will own the technical vision for how the team works, not just what it builds, and you will be accountable for demonstrable improvements in throughput, cycle time, and engineering quality.
This role sits at the intersection of applied AI, platform architecture, and engineering culture. It demands someone who is equally comfortable white\-boarding an agentic workflow architecture with senior engineers, hands\-on in the toolchain, and presenting a productivity roadmap to executive leadership. If you are driven by the idea of making an elite team of AI developers dramatically more effective—through systems, automation, and architecture rather than headcount—this role is for you.
Responsibilities:
Developer Workflow Automation \& Agentic Architecture
- Workflow automation: Architect and implement end\-to\-end automation of developer workflows—from requirements and planning through coding, review, testing, and delivery—using AI tooling and agentic systems to eliminate manual toil and accelerate throughput.
- Agentic workflow design: Design and deploy multi\-step agentic pipelines that augment developer capacity: autonomous code generation and review agents, intelligent test synthesis, automated documentation, and context\-aware developer assistance integrated directly into the engineering environment.
- Toolchain architecture: Own the architectural decisions behind the developer toolchain—coding assistants, AI\-augmented IDEs, autonomous agents, and supporting infrastructure—ensuring components are composable, maintainable, and purpose\-built for AI development workloads.
- Platform integration: Integrate AI productivity tooling deeply into the existing engineering platform—version control, issue tracking, code review, and runtime observability—so automation is ambient rather than opt\-in.
Velocity \& Productivity Strategy
- Productivity measurement: Define and own the metrics framework for engineering velocity and productivity—establishing baselines, tracking improvement over time, and connecting automation investments to measurable outcomes (cycle time, defect rate, deployment frequency, developer satisfaction).
- Technical roadmap: Develop and maintain a multi\-horizon productivity roadmap that anticipates the evolution of AI capabilities and continuously raises the ceiling on what the team can deliver.
- Prioritization: Identify the highest\-leverage automation and tooling opportunities across the LATC engineering organization and sequence investments for maximum near\-term and long\-term impact.
Organizational Leadership \& Influence
- Architecture authority: Set and govern standards for AI tooling and automation practices across the engineering organization; act as the final technical voice on developer productivity architecture decisions.
- Engineering culture: Champion an automation\-first engineering culture, coaching senior engineers and tech leads on agentic workflows, prompt engineering for developer contexts, and AI\-augmented development practices.
- Cross\-functional alignment: Partner with engineering leadership, product, infrastructure, and security to ensure productivity investments are well\-integrated, compliant, and aligned with broader organizational direction.
- Executive communication: Translate productivity strategy and outcomes into clear, business\-grounded narratives for senior leadership; surface blockers and decisions that require executive attention.
- Ecosystem intelligence: Maintain a continuously updated view of the external AI developer tooling landscape—evaluating emerging tools, frameworks, and agentic architectures—and feed that intelligence into roadmap decisions.
Required Qualifications:
- Master’s degree in Computer Science, Engineering, or a related technical field.
- 15\+ years of experience in software engineering, platform engineering, or developer productivity roles, with at least 3 years at a principal, staff, or architect level.
- Demonstrated experience designing and delivering large\-scale developer tooling, platform, or automation programs that produced measurable gains in engineering velocity.
- Deep hands\-on expertise with AI coding assistants, agentic workflow frameworks, and the emerging landscape of autonomous development tools.
- Strong architectural background spanning developer toolchains, automation pipelines, and the infrastructure that underpins them—able to make and defend principled technology choices at scale.
- Proven ability to lead through influence in a large, matrixed organization—building alignment across engineering teams, platform owners, security, and senior leadership without direct authority.
- Hands\-on programming experience in one or more modern languages (e.g., Python, Go, TypeScript); fluency in the tools and workflows of professional software development.
- Strong command of engineering productivity metrics and the ability to build measurement frameworks that hold up under scrutiny.
- Executive\-level communication skills: able to synthesize complex technical strategy into clear, decision\-ready narratives for VP\- and C\-level audiences.
Preferred Qualifications:
- Experience building or operating agentic AI systems in a production engineering context—including multi\-agent orchestration, tool use, and retrieval\-augmented workflows.
- Background in AI/ML infrastructure or MLOps, with an understanding of the unique workflow pressures faced by AI research and engineering teams.
- Prior experience in a platform engineering leadership role with broad authority over toolchain standards and developer experience.
- Experience with organizational change management and engineering culture transformation at scale.
- Familiarity with Lenovo’s internal structure or similar large\-scale, globally distributed technology organizations.
\#latc
*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 \- California \- San Jose
- United States of America
- United States of America \- California
- United States of America \- California \- San Jose
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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Lenovo AI Hiring
Lenovo has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, AI Engineering Manager. Positions span Morrisville, NC, US, San Jose, CA, US. Compensation range: $210K - $300K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 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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