AI Infra Staff 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

• 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).

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* 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 Staff Researcher in AI Compute and Data Infrastructure will conduct applied research and hands-on development for intelligent, efficient, and resilient Hybrid AI systems. This position works across AI algorithms, computer systems, distributed computing, and data infrastructure to address performance, scalability, reliability, and energy-efficiency challenges.

The successful candidate will independently own substantial research and development work streams, build production-quality software, characterize AI workloads, diagnose infrastructure issues, and develop cross-layer optimization technologies spanning GPUs and other accelerators, CPUs, memory, storage, networking, system software, data pipelines, and AI frameworks.

• Research and develop technologies for AI compute and data infrastructure, distributed AI systems, and intelligent infrastructure management.
• Design and implement production-quality software, system components, services, APIs, diagnostic tools, and scalable data-processing pipelines.
• Characterize AI training, inference, and data-processing workloads using profiling, tracing, benchmarking, telemetry, logs, and hardware performance counters.
• Diagnose performance bottlenecks and reliability issues across GPUs, accelerators, CPUs, memory hierarchy, storage, networking, operating systems, runtimes, and AI frameworks.
• Develop hardware/software co-optimization solutions for GPU utilization, workload scheduling, resource allocation, memory and cache management, communication, data movement, storage access, and model execution.
• Optimize large-scale data ingestion, preprocessing, transformation, storage, retrieval, and delivery for AI training, inference, and analytics workloads.
• Build intelligent infrastructure diagnostics for anomaly detection, root-cause analysis, performance regression detection, system health assessment, capacity forecasting, and predictive maintenance.
• Develop fault-tolerance and resilience mechanisms, including fault detection and isolation, checkpointing, recovery, retry, failover, graceful degradation, and automated remediation.
• Apply machine learning and deep learning to workload modeling, performance prediction, resource optimization, failure prediction, and operational decision-making.
• Apply time-series analysis and signal processing to infrastructure telemetry, event detection, change-point detection, workload forecasting, and system health monitoring.
• Apply causal inference to performance attribution, root-cause analysis, intervention evaluation, and infrastructure optimization.
• Develop knowledge graphs to model infrastructure topology, hardware/software dependencies, workloads, operational events, and failure relationships.
• Optimize systems for throughput, latency, scalability, availability, resource utilization, energy consumption, and total cost of ownership.
• Collaborate with hardware, systems, software, architecture, and product teams to transition research technologies into Enterprise AI and Personal AI products.
• Contribute to patents, invention disclosures, technical publications, internal reports, and reusable software assets.
• Provide technical guidance and mentorship to junior researchers and engineers.

• Master's or PhD degree in computer science, computer engineering,…