Data Center LLM Optimization Customer Engineer

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Company:

Qualcomm China

## Job Area:

Engineering Group, Engineering Group > Software Applications Engineering

General Summary:

The Data Center Customer Engineering team is seeking an experienced ML/Systems Engineer specializing in LLM inference optimization. This role works directly with strategic customers, partners, and internal engineering teams to deploy and optimize generative AI workloads on Qualcomm Cloud AI inference accelerators. The engineer will translate model-level requirements into efficient accelerator, memory, communication, and cluster configurations while driving measurable improvements in throughput, latency, scalability, power efficiency, and production reliability.

# Key Responsibilities

· Deploy and optimize LLM and multimodal inference workloads on Qualcomm Cloud AI accelerator platforms from single accelerator to multi-node/rack-scale configurations.

· Profile and optimize prefill and decode performance, including Time to First Token (TTFT), Time per Output Token (TPOT), inter-token latency, tokens/second, throughput per user, and performance per watt.

· Analyze model architecture and runtime bottlenecks across compute, accelerator memory, host memory, KV cache, interconnect, networking, and software stack.

· Optimize model execution using techniques such as quantization (FP16/BF16/FP8/INT8/INT4 as platform-supported), batching, continuous/dynamic batching, KV-cache management, paged attention, prompt/context-length tuning, and speculative decoding where applicable.

· Optimize distributed inference across tensor, pipeline, data, context, and expert parallelism; characterize collective communication including AllReduce, AllGather, ReduceScatter, and AllToAll.

· Evaluate and tune Scale-Up and Scale-Out performance, including topology-aware workload placement, network bandwidth/latency, communication-compute overlap, and scaling efficiency.

· Design and execute reproducible benchmark and stress-test methodologies across model size, batch size, input/output sequence length, concurrency, accelerator count, and node count; identify saturation points and performance regressions.

· Bring up and validate customer LLMs and representative open-source models; establish performance baselines, optimization plans, and competitive/production-readiness reports.

· Partner with runtime, compiler, firmware, driver, platform, networking, and model teams to debug cross-layer performance issues and drive root-cause resolution.

· Build Python/Bash-based profiling, benchmarking, automation, and data-analysis tooling; document reusable optimization recipes, reference configurations, and best practices.

· Work directly with customers during evaluation, PoC, design-win, and production phases; convert workload requirements into clear engineering actions and communicate performance trade-offs to technical and business stakeholders.

# Required Qualifications

· Master’s degree or higher in Computer Science, Electrical/Computer Engineering, AI/ML, or a related technical field, with strong hands-on systems and performance engineering experience.

· Strong understanding of Transformer/LLM inference, including attention, MHA/GQA/MQA, feed-forward networks, KV cache, prefill vs. decode behavior, and memory/computation characteristics.

· Hands-on experience deploying and profiling LLM workloads using Python and at least one major ML framework such as PyTorch; ability to diagnose performance using traces, profiler data, logs, and system telemetry.

· Experience with model optimization techniques such as quantization, batching, memory optimization, kernel/runtime tuning, and graph/compiler optimization.

· Good understanding of distributed AI concepts, including multi-accelerator execution, collective communication, Scale-Up/Scale-Out fabrics, and common parallelism strategies.

· Strong Linux skills and familiarity with containers and orchestration concepts (Docker/Kubernetes) used in datacenter AI deployments.

· Strong scripting and automation skills in Python and Bash; disciplined approach to benchmark reproducibility and performance-data analysis.

· Excellent debugging, problem-solving, and customer-facing communication skills, with the ability to work across multiple engineering organizations.

# Preferred Qualifications

· Experience with high-performance LLM serving stacks or optimization frameworks such as vLLM, TensorRT-LLM, SGLang, Hugging Face Transformers, DeepSpeed, or equivalent accelerator-specific runtimes.

· Experience with model families such as Llama, Qwen, DeepSeek, Mixtral, or other dense/MoE LLMs and multimodal models.

· Knowledge of MoE architectures, expert parallelism, token routing, and AllToAll communication performance.

· Experience with RDMA/RoCE/InfiniBand or Ethernet-based AI fabrics, NUMA/topology awareness, and distributed performance analysis.

· Familiarity with MLPerf Inference methodology, production observability, CI/CD performance regression testing, and capacity planning.

· Experience supporting customer PoCs or production deployments of AI accelerators at server, rack, or cluster scale.

Minimum Qualifications:

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Applications Engineering, Software Development experience, or related work experience.

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Applications Engineering, Software Development experience, or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Applications Engineering, Software Development experience, or related work experience.

• 2+ years of experience with Programming Language such as C, C++, Java, Python, etc.
• 1+ year of experience with debugging techniques.

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