AI Infra Advisory Researcher

General Information

Req #: WD

Career area:
Research/Development

Country/Region:
United States of America

State:
North Carolina

City:
Morrisville

Date:
Monday, August 10, 2026

Working time:
Full-time

Additional Locations:
United States of America
- North Carolina
- Morrisville

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 , and read about the latest news via our Story Hub.

* Please Note
* This is a hybrid role in Morrisville, NC. This candidate will be required to work onsite three days a week.

This candidate MUST be a US citizen or US national; US permanent residents or candidates requiring sponsorship cannot be considered.

The Advisory Researcher in AI Compute and Data Infrastructure will provide senior technical leadership for the research, architecture, and development of intelligent, high-performance, and resilient Hybrid AI systems. This position combines research depth, hands-on software development, system architecture expertise, and the ability to translate emerging technologies into production infrastructure and differentiated product capabilities.

The successful candidate will define technical approaches, lead complex research initiatives, and make architecture-level decisions across GPUs and other accelerators, CPUs, memory, storage, networking, distributed systems, data platforms, AI frameworks, and application workloads.

• Define technical directions and lead major research and development initiatives in AI compute and data infrastructure, distributed AI systems, and intelligent infrastructure management.
• Identify high-impact technical opportunities based on infrastructure challenges, emerging technologies, product requirements, and business value.
• Architect end-to-end AI infrastructure solutions spanning hardware, system software, data platforms, distributed training and inference, and application workloads.
• Lead hardware/software co-analysis and co-optimization across GPUs, accelerators, CPUs, memory hierarchy, storage, networking, runtimes, frameworks, and AI applications.
• Define optimization strategies for GPU utilization, workload placement, resource orchestration, memory and cache efficiency, communication, data movement, storage access, and model execution.
• Lead the architecture and optimization of large-scale data pipelines for data ingestion, preprocessing, transformation, storage, retrieval, and delivery to AI workloads.
• Define intelligent observability and diagnostic technologies for anomaly detection, root-cause analysis, performance regression, capacity planning, system health assessment, and predictive maintenance.
• Develop resilient and fault-tolerant infrastructure architectures, including failure isolation, checkpointing and recovery, redundancy, retry, failover, graceful degradation, and automated remediation.
• Apply machine learning and deep learning to system modeling, infrastructure control, workload forecasting, resource optimization, failure prediction, and operational intelligence.
• Apply time-series modeling and signal processing to telemetry analytics, event detection, change-point detection, capacity forecasting, and system health monitoring.
• Apply causal inference and causal discovery to root-cause analysis, performance attribution, intervention evaluation, and automated decision-making.
• Define knowledge graph architectures for modeling infrastructure topology, hardware/software dependencies, workloads, operational events, and failure relationships.
• Make architecture-level trade-offs involving performance, scalability, reliability, availability, energy consumption, cost, security, and maintainability.
• Lead technical design reviews, architecture reviews, performance investigations, and resolution of complex cross-layer system issues.
• Provide hands-on technical guidance in algorithm design, software implementation, system optimization, experimental validation, and production deployment.
• Establish reusable frameworks, engineering…