AI Infra Staff Researcher

Lenovo•Morrisville, NC

1d•Hybrid

About The Position

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.

Requirements

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.
Must be a US citizen or US national; US permanent residents or candidates requiring sponsorship cannot be considered.

Nice To Haves

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.

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.