AI Infra Advisory Researcher

Lenovo•Morrisville, NC

1d•Hybrid

About The Position

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.

Requirements

Bachelor's degree in computer science, computer engineering, artificial intelligence, electrical engineering, applied mathematics, or a related field, or equivalent practical experience.
Five 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.
Demonstrated leadership of technically complex research, architecture, or system development initiatives.
Strong hands-on programming and system architecture capabilities.
Deep expertise in at least three of the following areas: Machine learning or deep learning, GPU or accelerator optimization, Distributed AI training and inference, Large-scale and streaming data processing, Hardware/software co-optimization, Time-series modeling or signal processing, Infrastructure diagnostics, reliability, and fault tolerance, Causal inference, Knowledge graphs or graph-based reasoning.
Proven record of delivering advanced software, infrastructure, or research technologies into products or production environments.
Ability to resolve ambiguous, cross-layer technical problems and make informed architecture-level trade-offs.
Strong technical communication, mentorship, and cross-organizational influence.
Must be a US citizen or US national; US permanent residents or candidates requiring sponsorship cannot be considered.

Nice To Haves

Experience architecting enterprise-scale cloud, edge, on-premises, or hybrid AI infrastructure.
Experience with PyTorch, TensorFlow, JAX, CUDA, ROCm, Spark, Flink, Ray, Kafka, Kubernetes, or related technologies.
Experience with MLOps, observability platforms, distributed computing, data lakehouse architectures, or cloud-native systems.
Strong publication record, patent portfolio, open-source contributions, or demonstrated commercial product impact.

Responsibilities

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 practices, evaluation methodologies, and technical standards for AI infrastructure development.
Partner with research, engineering, architecture, product, and business teams to transition research into Enterprise AI and Personal AI platforms and products.
Mentor Staff Researchers and engineers and contribute to patents, publications, technical standards, and differentiated intellectual property.