AI/Machine Learning Engineer (Embedded Systems, Inference Efficiency)

As a member of the Low Power AI Solution team, you will conduct advanced research on model efficiency, model compression techniques, and ML system optimization to push the boundaries of efficient on‑device inference. You will lead and contribute to high-impact research initiatives, understand hardware–software interactions at a fundamental level, and collaborate with global teams to develop systems that shape future Qualcomm AI accelerator capabilities. New Position

Key Responsibilities

• Conduct cutting-edge research in inference efficiency and ML system optimization: efficient architecture design, model compression, PEFT, compiler stack optimization etc.
• Prototype and develop system solutions with software–hardware co-design to align architectural choices, dataflows, and memory behavior with Qualcomm’s low-power AI accelerators for optimal model deployment
• Collaborate closely with modeling, compiler, and hardware teams to convert research into production-ready low power AI solutions, enabling real-world applications and commercial impact.
• Influence future accelerator features and model deployment and contribute to Qualcomm’s strategic initiatives in efficient AI and embedded intelligence.

Requirements

• Proven research excellence on inference efficiency and ML system, demonstrated by publications, community contributions, or equivalent evidence of impact.
• Deep expertise in neural network architectures, model compression (e.g., quantization, pruning, knowledge distillation) and efficient inference algorithm
• Strong background on compiler stack and ML system optimization for AI accelerators (e.g., graph transformation, graph tiling and scheduling, tensor layout/memory optimization)
• Strong understanding of Machine Learning fundamentals, strong programming skills with ML frameworks
• Hands-on experience with model development pipelines for AI accelerator, including training, fine-tuning, evaluation, and performance optimization.

Preferred Qualifications

• PhD in Computer Science, Electrical Engineering, or related fields or MS with AI research, or related work experience.
• Extensive experience in deep learning research and impactful publications in top-tier machine learning venues (NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP etc.).
• Experience in on-device model deployment and optimization algorithms for AI hardware accelerators
• Experience working with a variety of stakeholders and ability to communicate complex outcomes to a wide range of audiences.