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

- **Company:** Qualcomm
- **Location:** San Diego, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $194,400.0 - $291,600.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, Artificial Neural Networks, C++ (Programming Language), Information Systems, Computer Programming, Computer Engineering, Software Debugging, Linux, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Software Systems, Deep Learning, Agentic-AI, Information Technology, Embedded AI, Software Performance, Programming Languages
- **Published:** October 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=665a5a78fbd28722

## About the Role

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., * 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.
OR
PhD in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience., * 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.

## Description

* 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.
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