Performance & Modeling Engineer
Qualcomm · Beijing, China +2 · 1d ago
Company:
Qualcomm China
Job Area:
Engineering Group, Engineering Group > Software EngineeringGeneral Summary:
Qualcomm Cloud AI is seeking an experienced Performance & Modeling Engineer to deploy, enable, benchmark, and optimize state-of-the-art AI models on Qualcomm Cloud AI platforms. In this role, you will leverage Qualcomm Cloud AI toolchains to rapidly bring up industry-leading AI workloads, analyze performance bottlenecks, drive model accuracy and performance optimization, and deliver best-in-class inference efficiency for cloud-scale AI deployments.
Key Responsibilities
- Deploy and optimize LLM, MoE, and multimodal models on Qualcomm Cloud AI platforms.
- Analyze and improve model accuracy, latency, throughput, memory utilization, and scalability.
- Drive graph-level, framework-level, and system-level optimizations.
- Benchmark AI workloads and identify performance bottlenecks across the software and hardware stack.
- Work closely with compiler, runtime, kernel, and architecture teams to improve model enablement and performance.
- Support customer deployment, bring-up, performance tuning, and production readiness.
Required Qualifications
- Master's degree or above in Computer Science, Artificial Intelligence, Mathematics, Electrical Engineering, or related fields.
- Good verbal and written English communication skills to effectively collaborate across global teams.
- Strong experience with Python, C++, and Linux development environments.
- 5+ years of experience in Cloud AI, LLM deployment, or AI infrastructure.
- Deep understanding of Transformer architectures, LLMs, and MoE models.
- Strong experience in graph optimization and model compilation workflows.
- Strong experience in model accuracy and performance optimization.
- Strong experience with one or more AI frameworks, including PyTorch, ONNX, TensorFlow, or JAX.
- Experience with AI serving frameworks such as vLLM, SGLang, TGI, TensorRT-LLM, or LMDeploy.
Preferred Qualifications
- Familiarity with AI accelerator platforms and large-scale inference systems.
- Experience with Quantization, KV-Cache optimization, speculative decoding, continuous batching, Prefix Caching, Parallelism Strategies and distributed inference.
- Experience with performance modeling and analysis methodologies, including Roofline analysis, bottleneck identification across software and hardware stacks.
- Familiarity with open-source inference frameworks such as llama.cpp, Ollama, or MLC-LLM.
- Experience with Kubernetes, Docker, and cloud-native AI infrastructure.
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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