Senior Staff Engineer, Machine Learning Engineering ( Agentic AI SW )

Qualcomm•San Diego, CA

3d•$194,400 - $291,600

About The Position

Qualcomm AI Research is seeking an experienced software engineer to help advance state-of-the-art AI technologies on edge devices. This is an opportunity to join a world-class team developing industry-leading Generative AI solutions, model optimization technologies, and AI runtime software that enable deployment of cutting-edge models on edge devices with optimal performance, power efficiency, memory utilization, and computational efficiency. In this role, you will be part of a multidisciplinary team that has consistently delivered industry-first innovations on Qualcomm AI accelerators. Recent achievements include the world's first on-device text-to-image generation using Stable Diffusion, power-optimized Vision Language Models (VLMs), and on-device large language model deployment, including 7B-parameter class models. You will contribute to the design, development, optimization, and deployment of next-generation AI and agentic AI technologies that push the boundaries of on-device intelligence. The ideal candidate will possess a strong software engineering background with experience leading complex technical initiatives, mentoring engineers, and collaborating across research, software, and hardware organizations. Success in this role requires the ability to operate independently, adapt to evolving priorities, solve challenging technical problems, and drive execution against ambitious product and research milestones.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
Master's degree in Computer Science, Engineering, Information Systems, or related field and 5+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
PhD in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

Nice To Haves

At least 9 years of work experience in a relevant field.
5+ years of experience and excellent programming capabilities with C/C++ and Python.
5+ years of experience with low level interactions between operating systems (e.g., Linux, Android, QNX) and Hardware.
5+ years of experience in embedded system development and optimization with application to a specific problem domain in ML (e.g., NLP, multi-media).
5+ years of experience with one or more programming language suitable for machine learning (e.g., Python, R, C, C++)
Knowledge of model onboarding and on device inference.
Familiarity with software performance and power optimization
Experience with the Qualcomm AI Software Stack + SDKs is a plus.

Responsibilities

Lead the development of end-to-end embedded AI software and runtime systems to orchestrate neural network models on Qualcomm’s leading-edge hardware, driving the co-design of machine learning software and hardware for optimized inference and training solutions.
Design, develop, debug, profile, and optimize AI/ML frameworks, runtime software, model efficiency tools, compiler technologies, and machine learning inference to improve performance, power efficiency, scalability, and enable advanced hardware capabilities.
Develop, adapt, and prototype novel AI/ML and agentic software solutions aligned with product roadmaps and system-level innovation, translating advanced machine learning research into production-ready technologies and customer-facing solutions.
Provide technical leadership for the design, implementation, training, evaluation, and deployment of machine learning solutions, guiding experimentation, architecture decisions, and best practices across the team.
Design and enhance the implementation of ML/AI SW stack and runtime software to improve performance and power efficiency.
Collaborate cross-functionally with hardware, software, and research teams to define technical direction, solve complex problems, influence key organizational decisions, and drive successful delivery of strategic AI initiatives.
Work independently with minimal supervision while mentoring and providing technical guidance to team members, effectively communicating complex concepts to both technical and leadership audiences.