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

Job Title

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

Role Summary

Join the Low Power AI Solution team to research and develop methods that improve on-device inference efficiency. Work focuses on model efficiency, compression techniques, ML system optimization, and software–hardware co-design for Qualcomm AI accelerators.

The role converts research into production-ready solutions by collaborating with modeling, compiler, and hardware teams to influence accelerator features and enable low-power AI deployments.

Experience Level

Mid-level — typical experience: Bachelor's +2+ years, Master's +1+ year, or PhD (no minimum years). Candidates should have prior industry or research experience in ML model efficiency or system-level optimization.

Responsibilities

Focus on advancing inference efficiency and integrating solutions with accelerator hardware and software.

• Conduct research on model efficiency, model compression (quantization, pruning, distillation), PEFT, and ML system optimization.
• Prototype and implement system solutions using software–hardware co-design to optimize dataflows, memory behavior, and architectural choices for low-power accelerators.
• Optimize compiler and runtime stacks (graph transformation, tiling/scheduling, tensor layout and memory optimization) for efficient inference.
• Collaborate with modeling, compiler, and hardware teams to move research into production-ready deployments.
• Influence future accelerator features and contribute to strategic initiatives in efficient AI and embedded intelligence.

Requirements

Core technical skills and evidence of impact required; degree specifics are listed under Education Requirements.

• Demonstrated research impact in inference efficiency or ML systems (publications, community contributions, or equivalent evidence).
• Deep expertise in neural network architectures and model compression techniques (quantization, pruning, knowledge distillation).
• Strong background in compiler stack and ML system optimization for AI accelerators, including graph transformation, graph tiling/scheduling, and tensor layout/memory optimization.
• Solid understanding of machine learning fundamentals and strong programming skills with ML frameworks (training, fine-tuning, evaluation).
• Hands-on experience with model development pipelines targeting AI accelerators, including performance profiling and optimization for deployment.
• Ability to work across teams and communicate technical outcomes to engineering and product stakeholders.

Education Requirements

Minimum qualifications include one of the following: Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field with 2+ years of relevant experience; OR Master's degree in those fields with 1+ year of relevant experience; OR PhD in Computer Science, Engineering, Information Systems, or a related field. Preferred qualifications reference advanced degrees (MS or PhD) with AI research experience. Equivalent relevant work experience is accepted as described by the minimum qualification combinations.

About the Company

Company: Qualcomm

Headquarters: San Diego, California, United States

Qualcomm is a global leader in semiconductor and telecommunications equipment, specializing in mobile technologies and innovations. Known for its Adreno GPUs, the company provides solutions enabling advancements in mobile gaming, AI, VR/AR, and autonomous driving. Qualcomm's cutting-edge technology and commitment to high-performance, power-efficient designs drive the evolution of mobile graphics and connectivity worldwide.