AI Infra Engineer

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 #196 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).

Lenovo's CTO (Chief Technology Office) Org-Sustainable Computing Research Team is seeking an AI Infra Engineer, to support the delivery of cutting edge and energy efficient product offerings. The team focuses on software-hardware co-design for energy-efficient computing clusters, covering DVFS, intelligent task scheduling, liquid cooling optimization, and more. Our primary internal customer is Lenovo's Infrastructure Solution Group.

In this role, you will act as the bridge between our R&D team and Lenovo's global business teams. Your core mission is to localize and adapt our sustainable computing technologies, integrate them into Lenovo AI infra & device products, and support business deployment. You will also represent Lenovo in industry alliances such as OCP (Open Compute Project) to track and influence cutting-edge trends in green computing.

This position requires a hybrid schedule of 3 days onsite a week with two days remote.

Responsibilities:

• Lead AI data platform research for large-scale enterprise infrastructure data.
• Build prediction and forecasting models for time-series signals, operational metrics, and infrastructure events.
• Develop graph-based models to represent entities, dependencies, topology, and causal relationships across infrastructure systems.
• Apply Bayesian networks, causal inference, Markov models, and graph neural networks for anomaly detection and root-cause localization.
• Use LLMs to automate data cleaning, labeling, metadata generation, document parsing, quality validation, and knowledge extraction.
• Optimize large-scale data pipelines for throughput, latency, reliability, scalability, and cost efficiency.
• Explore GPU-accelerated data processing and model execution to improve computational efficiency.
• Define rigorous evaluation metrics for model accuracy, data quality, system performance, and business impact.
• Collaborate with global research, engineering, product, and business teams to deliver platform capabilities.

Required Qualifications:

• Minimum 5 years of hands-on experience in data science, machine learning, applied AI, or AI platform research.
• Strong expertise in machine learning, deep learning, probabilistic modeling, causal inference, and time-series forecasting.
• Strong experience with graph analytics, knowledge graphs, graph databases, ontology modeling, entity resolution, or graph neural networks.
• Strong programming capability in Python, SQL, and distributed data processing.
• Hands-on experience with Spark, PySpark, SparkSQL, Hadoop, or cloud-based big data platforms.
• Experience with Apache Iceberg, Delta Lake, Hudi, Trino, Presto, or equivalent lakehouse technologies.
• Experience with vector databases, embeddings, semantic search, and LLM-based data workflows.
• Experience with CUDA programming, GPU-accelerated computing, NVIDIA libraries, kernel optimization, memory optimization, or GPU performance profiling.
• Experience with Git, Docker, CI/CD, model deployment, monitoring, and production AI systems.

Preferred Qualifications:

• Master's degree or above in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related technical field.
• Strong ability to define ambiguous research problems and deliver measurable technical outcomes.
• Strong English communication skills for technical collaboration with global teams.
• Preferred additional strengths include AI workload benchmarking, vLLM, SGLang, TensorRT-LLM, publications, patents, open-source contributions, or technical leadership in AI data platforms.