Company
Qualcomm China
Job Area
Engineering Group, Engineering Group > Software Engineering
General Summary
Qualcomm Cloud AI is seeking an experienced Software Integration & Test Engineer to enable, validate, benchmark, and optimize large-scale AI workloads on Qualcomm Cloud AI accelerator platforms. You will work closely with architecture, runtime, compiler, kernel, and system software teams to deliver industry-leading AI performance and deployment readiness.
Key Responsibilities
Integrate, deploy, and validate AI software stacks on Qualcomm Cloud AI platforms.
Enable, deploy and evaluate state-of-the-art AI models, including LLMs, MoE, and multimodal models.
Develop and execute functional, performance, and regression testing.
Analyze performance bottlenecks and drive system-level optimization.
Build and maintain automated test and validation frameworks.
Collaborate across teams to ensure software quality, scalability, and release readiness.
Support customer deployment, bring-up, performance tuning, debugging, and production readiness.
Required Qualifications
Master's degree or above in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field.
Good verbal and written English communication skills to effectively collaborate across global teams.
Strong experience with Python and Linux development environments.
5+ years of experience in Cloud AI, AI infrastructure, machine learning systems, or high-performance computing.
Show the full description
Hands-on experience deploying and optimizing large language models in cloud environments.
Strong understanding of Transformer architectures, LLMs, and MoE models.
Experience with AI benchmarking, performance analysis, and workload characterization.
Strong experience with one or more AI frameworks, including PyTorch, TensorFlow, ONNX, or JAX.
Strong debugging, analytical, and problem-solving skills.
Preferred Qualifications
Experience with inference frameworks such as vLLM, SGLang, TensorRT-LLM, TGI, LLM-D, or similar solutions.
Experience with distributed inference, model serving, and large-scale AI deployments.
Familiarity with model optimization techniques such as quantization, KV-cache optimization, and speculative decoding.
Experience with Kubernetes, Docker, and cloud-native AI infrastructure.
Experience with AI accelerator platforms (GPU, NPU, TPU, or custom AI hardware).
Experience developing automated validation, regression, or CI/CD systems.
Minimum Qualifications
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
- OR
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.
- OR
- PhD in Engineering, Information Systems, Computer Science, or related field.
2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.
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