Edge AI Systems Engineer

EQ Semi; Saudi Arabia; On-site; SAR 299,962 - 374,953; Full time

Job summary

EQ Semi is seeking an Edge AI Systems Engineer in Saudi Arabia to architect intelligent solutions for edge devices across IoT, robotics, and automotive platforms. The role involves integrating neural network accelerators with CPUs/GPUs, developing firmware for real-time AI inference, and collaborating with teams to optimize system performance. Ideal candidates should have a BS/MS in Electrical Engineering or Computer Science, along with 5+ years of experience in embedded systems and familiarity with AI frameworks and low-level programming in Linux and RTOS environments.

Qualifications

• 5+ years in embedded systems or edge computing.
• Familiarity with AI frameworks and hardware accelerators.
• Experience with Linux, RTOS, and low-level programming.

Responsibilities

• Integrate neural network accelerators with CPUs/GPUs and optimize system-level performance.
• Develop firmware and drivers for real-time inference.
• Collaborate with hardware and software teams to meet latency and power goals.

Skills

Embedded systems

AI frameworks

Low-level programming

Linux

RTOS

Education

BS/MS in Electrical Engineering, Computer Science, or related field

Job description

We are looking for an Edge AI Systems Engineer to architect intelligent solutions for edge devices across IoT, robotics, and automotive platforms. You’ll be responsible for integrating neural network accelerators with CPUs and GPUs, optimizing system-level performance, and developing firmware and drivers for real-time AI inference. The ideal candidate has experience in embedded systems, AI frameworks, and low-level programming in Linux and RTOS environments.

Qualifications

BS/MS in Electrical Engineering, Computer Science, or related field.

5+ years in embedded systems or edge computing.

Familiarity with AI frameworks and hardware accelerators.

Experience with Linux, RTOS, and low-level programming.

Key Responsibilities

Integrate neural network accelerators with CPUs/GPUs and optimize system-level performance.

Develop firmware and drivers for real-time inference.

Collaborate with hardware and software teams to meet latency and power goals.