Lenovo

*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.

Position Overview

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 workstreams, 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.

Key Responsibilities

• 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.

Minimum Qualifications

• Bachelor's degree in computer science, computer engineering, artificial intelligence, electrical engineering, applied mathematics, or a related field, or equivalent practical experience.
• Three or more years of relevant experience in AI compute and data infrastructure, machine learning systems, distributed systems, data platforms, performance engineering, reliability engineering, or advanced software development.
• Strong programming skills in Python, C++, Java, Go, Rust, Scala, or a comparable language.
• Demonstrated ability to design, implement, test, debug, profile, and optimize reliable software systems.
• Experience with system profiling, telemetry analytics, observability, performance diagnosis, or failure analysis.
• Technical expertise in at least two of the following areas:
  • Machine learning or deep learning
  • GPU or accelerator optimization
  • Distributed training or inference systems
  • Large-scale data processing
  • Hardware/software co-optimization
  • Time-series modeling or signal processing
  • Infrastructure reliability and fault tolerance
  • Causal inference
  • Knowledge graphs or graph machine learning
• Strong analytical, experimental, communication, and cross-functional collaboration skills.

Preferred Qualifications

• Experience with PyTorch, TensorFlow, JAX, CUDA, ROCm, Spark, Flink, Ray, Kafka, Kubernetes, or related technologies.
• Experience with cloud, edge, on-premises, or hybrid AI infrastructure.
• Experience delivering research prototypes or advanced software into production environments.
• Publications, patents, open-source contributions, or demonstrated product impact.