Data Center LLM Optimization Customer Engineer
Qualcomm
Data Center LLM Optimization Customer Engineer
Qualcomm
Designs mobile chips and licenses wireless patents
Qualcomm China
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.
· 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.
· 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.
· 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.Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail [email protected] or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
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Qualcomm designs the Snapdragon processors and modems inside many Android phones, and it licenses its wireless patents to device makers around the world. It helped commercialize CDMA, the cellular technology behind 3G networks, and its patents cover core parts of the 4G and 5G standards that phones use to connect. The company does not run its own chip factories; contract foundries make its chips, which it sells to makers of phones, PCs, cars, and connected devices. Device makers also pay Qualcomm royalties for using its patented technology, which forms a second business alongside chip sales. Qualcomm was founded in 1985 and is based in San Diego, California.
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