Cloud and AI System Intern

Job Description:

Job Details:

Job Description:
In this position, you will work with a system reliability research team focusing on RAS (Reliability, Availability, Serviceability) and silent data error (SDE) characterization and mitigation on AI and general-purpose compute platforms, including heterogeneous systems (CPU + GPU/accelerators) and large-scale server clusters. You will help design and run experiments under representative AI training/inference and cloud workloads, analyze fleet-scale logs/telemetry, and prototype detection/diagnosis methods to improve end-to-end data integrity and platform robustness across the HW/FW/OS/runtime stack.
Your responsibilities 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:
Cloud and AI System Engineering Intern

Description
In this position, you will work on a system reliability research topic with platform engineering team focusing on RAS (Reliability, Availability, Serviceability) and silent data error (SDE) characterization and mitigation on AI and general-purpose compute platforms, including heterogeneous systems (CPU + GPU/accelerators) and large-scale server clusters. You will help design and run experiments under representative AI training/inference and cloud workloads, analyze fleet-scale logs/telemetry, and prototype detection/diagnosis methods to improve end-to-end data integrity and platform robustness across the HW/FW/OS/runtime stack.
Your responsibilities 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.

Job Type:
Student / Intern

Shift:
Shift 1 (China)

Primary Location:
PRC, Shanghai

Additional Locations:

Business group:
The Sales and Marketing Group (SMG) leverages the product portfolio to drive Intel's revenue growth and market expansion, blending strategic initiatives with dynamic sales efforts to capture and retain customers. SMG is responsible for empowering the sales force with tools and insights needed to close deals and build lasting customer relationships. Sales analytics and market research ensure strategies are both targeted and impactful. In SMG, disciplined execution, creativity, and ambition are celebrated, providing ample opportunities for career advancement and skill development.

Posting Statement:
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust
N/A

Work Model for this RoleThis role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.

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