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# High Availability / AI Infrastructure Software Engineer

- **Company:** JD.com International Limited
- **Location:** Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Nvidia CUDA, Data Structures, Distributed Systems, Fault Tolerance, Python (Programming Language), AI Infrastructure, Cloud Platform System, High Performance Computing, Pytorch, System Availability, Kubernetes, Information Technology
- **Published:** July 23, 2026
- **Apply:** https://es.indeed.com/viewjob?jk=0b4025c4af33cad7

## About the Role

3+ years of experience in AI infrastructure, distributed systems, High-Performance Computing (HPC), or large-scale cloud platform development. Proficient in Python, with a solid foundation in data structures and algorithms, and a rigorous coding style. Bonus points: Deep understanding of the underlying source code and runtime mechanisms of PyTorch, Megatron-LM, or DeepSpeed. Bonus points: Proficient in GPU/NPU architecture, with hands-on development and optimization experience in CUDA C/C++ or Triton. Strong self-drive and technical curiosity; ability to proactively track the most cutting-edge AI Infra trends in the industry. Bachelor's degree or higher in Computer Science, Electronic Information, Automation, Applied Mathematics, or related fields; experience in cross-border e-commerce is highly preferred.

## Description

Participate in building the AI Infrastructure technical architecture for JD.com's cross-border e-commerce; develop and drive the implementation of architectural standards. Lead the top-level architectural design for complex technical projects; evaluate critical technical issues and break down project execution steps.

Scheduling System Optimisation: Participate in the R&D of the intelligent computing operating system; Optimize GPU resource scheduling capabilities within Kubernetes or Ray.

High Availability Assurance: Resolve GPU cluster fault recovery (Fault Tolerance) and elastic fault tolerance (elastic checkpointing).

Efficiency & Observability: Build cluster efficiency evaluation models; precisely measure computing resource utilisation (MFU/HFU). Track industry trends and cutting-edge technologies; introduce new technologies and solutions to the team based on actual business needs.


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