As a Machine Learning/AI Engineering Intern at Qualcomm, you will develop and optimize machine learning tools and frameworks for edge devices, working with neural network architectures, ML compilers, and hardware accelerators. You'll collaborate with a multi-disciplinary team on projects spanning model compression, inference optimization, compiler technologies, and distributed ML systems for smartphones, autonomous vehicles, robotics, and IoT platforms.… The role requires proficiency in programming (C++, Python), deep learning frameworks (TensorFlow, PyTorch), and exposure to GPU acceleration, performance optimization, and large-scale ML deployment. You will design, develop, and test software that makes models smaller and more efficient for next-generation mobile and embedded machine learning platforms.
parallel-computing jobs in San Diego
Design, develop, and deploy AI/ML solutions leveraging Qualcomm's hardware products and ecosystem to optimize inference performance, power efficiency, and scalability. Develop Gen AI and LLM applications, implement fine-tuning and distillation techniques, perform model benchmarking and functional analysis, and optimize deployment strategies across heterogeneous computing environments.… Apply systems-level expertise to design AI architectures, validate algorithms, and collaborate across teams to deliver innovative AI solutions while staying current with advancements in models, hardware, and software.
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.
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.
Principal-level AI/ML Platform Architect responsible for designing and optimizing AI/ML systems across Qualcomm's hardware and software stacks, particularly for Windows PC/Laptop platforms. The role requires deep expertise in performance optimization, bottleneck analysis, and end-to-end system characterization across GPUs, NPUs, and multiple AI domains (computer vision, audio, generative AI, agentic AI).… Candidates must demonstrate 15+ years in high-performance computing systems engineering with 5+ years specifically in AI system optimization, and will interface with cross-functional teams to deliver reference implementations, tools, and documentation for third-party developers.
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.