Cloud and AI System Intern

What You'll Do

• will include but not be limited to: -Collect, clean, and analyze platform telemetry / error logs from CPU servers and accelerator-enabled nodes (e.g., memory/DDR/HBM, storage, interconnect, PCIe/CXL, fabrics) to identify error signatures and failure patterns. -Design and execute fault injection, stress tests, or workload-driven experiments to reproduce silent data corruption scenarios for AI training/inference and general compute workloads, and validate hypotheses. -Research and analyze in-field scan and lockstep mode features (coverage, limitations, trigger conditions, and impact on AI/CPU workloads), and help evaluate how they can be leveraged to improve silent error detection and data integrity in production. -Research and analyze Silicon Lifecycle Management (SLM) solutions, and integrate them with platform telemetry to enable in-field health monitoring, degradation/trend analysis, and proactive reliability improvements for AI/CPU platforms. -Develop scripts/tools (Python preferred) to automate data processing, experiment orchestration, and report generation; build dashboards or repeatable pipelines when needed. -Study and evaluate mitigation techniques for AI + CPU platforms (e.g., ECC/CRC/EDAC, scrubbing policies, retry/recovery, checkpoint/restart, end-to-end checks at data/communication boundaries) and quantify effectiveness vs. performance/cost impact. -Collaborate with cross-functional teams (HW, FW, OS, driver/runtime, datacenter operations) to trace error propagation paths and drive actionable improvements; document findings and present progress regularly.
• Qualifications Preference will be given to candidates who are interested in system reliability / data integrity research on AI and general-purpose compute platforms.
• The qualifications include but not limited to: -PHD students (CS/CE/EE/Math/Statistics or related majors). -Solid programming skills in Python; experience with Linux and basic scripting; familiarity with Github copilot is a plus. -Strong data analysis skills; experience with pandas/numpy/matplotlib, SQL, or log analytics is a plus. -Basic understanding of computer architecture and systems (memory hierarchy, storage, networking) is preferred; familiarity with RAS concepts (ECC, CRC, parity, scrubbing, checkpoints) is a plus. -Understanding of AI system stack is a plus: GPU/accelerators, driver/runtime, distributed training/inference, communication collectives, data pipelines, and performance/reliability trade-offs. -Good Mandarin and English communication skills are required, in both verbal and written. -Research mindset: ability to form hypotheses, design experiments, and write clear technical reports.