Qualcomm is seeking an AI Performance Engineer to optimize and deploy machine learning models for inference acceleration on cloud AI hardware. The role involves converting and optimizing language models, vision models, and diffusion models using PyTorch and ONNX, analyzing performance bottlenecks, and designing efficient kernels in Triton. You will collaborate with compiler, firmware, and platform teams to map next-generation AI workloads onto hardware, working across the full product lifecycle from research to commercial deployment. The position requires deep expertise in transformer architectures, inference optimization techniques, computer architecture, ML accelerators, and distributed systems.

Join Qualcomm AI Research as a Machine Learning Engineer to architect, design, develop, and deploy on-device AI prototype software for edge devices. You will work with a multi-disciplinary team using cutting-edge AI frameworks, focusing on model fine-tuning, quantization, and edge inference. Strong Python, deep learning frameworks (PyTorch), object-oriented design, and software engineering best practices are required. Preferred qualifications include generative AI knowledge, neural network optimization, NPU/ML accelerator experience, Android programming, C/C++, and embedded software skills.

As a Staff Product Data Manager for AI Platforms & Systems Solutions at Qualcomm, you will own the product strategy, definition, and lifecycle for AI platform products and specifications spanning model enablement, agentic systems, orchestration runtimes, and open protocols. You will develop comprehensive plans of record including schedules, budgets, and resources, directing products from conception through end of life while functioning as a central resource across systems, engineering, standards, quality, marketing, and partner teams. The role requires deep fluency in the Windows edge-AI stack, with technical depth across AI/ML models, agentic systems, orchestration engines, standards development, and hands-on experience delivering Windows-based AI products leveraging local accelerators (CPU, GPU, NPU). You will translate emerging model, agentic, and orchestration trends into differentiated product and specification roadmaps.

Design, develop, and optimize machine learning systems and models for production AI platforms, with focus on inference optimization, scalable ML infrastructure, and deployment. Responsibilities include building ML pipelines, optimizing model inference across hardware environments, integrating LLMs and models into APIs and microservices, designing data pipelines for ingestion and feature engineering, and collaborating cross-functionally on end-to-end ML solutions. Requires strong software engineering fundamentals combined with deep ML expertise, proficiency in Python and at least one systems language (C++, Rust, or Go), and solid understanding of ML frameworks, transformer architectures, and model deployment systems.