Design and optimize modem software for embedded systems, focusing on performance analysis, architectural trade-offs, and real-world workload optimization. Conduct pre-silicon evaluations, profile silicon behavior, and develop debugging and validation tools.… Apply data-driven analysis and machine-learning techniques to identify bottlenecks across multiple chipset generations and implement solutions through software optimization, cache tuning, and memory management.
Senior engineer responsible for designing, building, and optimizing large-scale AI platforms for wireless, 5G, and connected devices. You will architect end-to-end AI/ML pipelines, integrate models into chipsets and cloud/edge environments, and deliver scalable inference services, tools, and SDKs.… Key responsibilities include model deployment, performance tuning on heterogeneous hardware, MLOps automation, and monitoring across silicon, software, and product teams.
Develop, optimize, and validate AI/ML solutions that leverage Qualcomm's AI inference accelerators for data center and hybrid applications. Design and implement GenAI and LLM applications, perform model benchmarking, optimize deployment strategies, and drive system-level architecture decisions.… Apply deep expertise in ML frameworks, system performance profiling, and parallel computing to ensure best-in-class inference performance, power efficiency, and scalability across heterogeneous hardware.
Staff or Senior Staff Software Engineer role leading design and development of Qualcomm's AI Stack (QAIRT/Genie) to enable efficient execution of generative AI models and large language models on low-power Snapdragon platforms. Responsibilities include developing core runtime components for resource-constrained inference systems, optimizing performance/accuracy/power consumption, debugging complex issues, and mentoring junior engineers.… Requires 5–8+ years of software development experience with strong C/C++ and Linux proficiency, plus knowledge of hardware acceleration and embedded AI deployment.
Design and architect AI/ML platforms optimized for Qualcomm's hardware (GPU/NPU) and software stack, with focus on end-to-end performance analysis, algorithm tuning, and system bottleneck reduction. Analyze ML workloads across Computer Vision, Audio, Generative AI, and Agentic AI domains; correlate device performance against pre-silicon predictions; and develop reference implementations and tools for third-party developers.… Requires 15+ years in high-performance computing systems engineering or software with 5+ years in AI system optimization, strong expertise in AI frameworks and performance profiling, and ability to lead cross-functional teams and articulate complex technical concepts.
Develop and optimize machine learning software for Qualcomm's AI Stack SDKs (QAIRT, Genie), enabling efficient execution of generative AI models and large language models on low-power Snapdragon platforms. Design core runtime components for resource-constrained AI systems, validate performance/accuracy across use cases, and debug complex issues in embedded AI environments.… Requires C/C++ proficiency, Linux development experience, and strong software engineering fundamentals; 2+ years experience for senior roles, project/internship experience for entry-level. Preferred: embedded systems background, generative AI model familiarity, hardware acceleration knowledge, and experience with Qualcomm AI Stack products.
As a Senior Systems Engineer for Data Center AI at Qualcomm, you will research, develop, optimize, and validate AI/ML solutions that integrate Qualcomm's hardware accelerators with software and ecosystem to deliver high-performance inference in datacenter environments. You will design and deploy Gen AI and LLM applications, implement fine-tuning and distillation techniques, perform model benchmarking, and drive system-level architecture decisions.… You will collaborate across functional teams to meet system-level requirements while staying current with advances in AI/ML models and hardware. Required skills include strong Python proficiency, expertise in ML frameworks (PyTorch, TensorFlow), deep understanding of ML deployment and system performance profiling, and experience with parallel computing.