Arm
Principal Field Application Engineer - Edge AI
$196K–$264K
San Jose, California · Principal · Full-time · Posted Oct 1, 2026
- Experience
- Experience is required; no number of years is stated.
- Education
- Bachelor’s degree or equivalent experience in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field.
- Visa sponsorship
- The role does not meet sponsorship eligibility; the successful candidate must have the right to work in the US without relying on sponsorship by Arm.
About the job
Advises strategic partners on integrating Arm technologies and Edge AI, leading technical engagements from system architecture through implementation and deployment.
What you'll do
- Provide architectural guidance and help customers select, integrate, and optimize Arm IP and platforms.
- Lead customer-facing technical engagements, resolve complex cross-functional issues, and drive adoption and design wins.
- Share customer feedback, market insights, and competitive intelligence to influence product strategy and roadmaps.
What they ask for
- Bachelor’s degree or equivalent experience in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field.
- Experience in semiconductor, SoC, applications engineering, system architecture, engineering sales, or related technical roles.
- Strong understanding of SoC architecture, system integration, hardware/software interactions, and Arm-based systems.
- Background in customer-facing technical roles and experience leading technical discussions on architecture and implementation trade-offs.
Nice to have
- Master’s degree in a related field; experience with Arm Compute Subsystems (CSS), AI/ML accelerators, or advanced SoC architectures.
- Experience developing or supporting Edge AI solutions on embedded and edge platforms.
Pay
$195,500-$264,500 per year (USD; base salary)
Tools
Arm-based SoCs, CPUs, GPUs, NPUs, Compute Subsystems, system IP, AI/ML accelerators, SoC integration
AI focus
Supports Edge AI solutions and AI/ML workloads on embedded and edge platforms, including guidance on Arm-based systems and accelerators.
Model-written summary · the original posting is authoritative