Embedded AI Engineer Intern: Robust FPGA & MCU Solutions

AUMOVIO; Singapore; On-site; SGD 20,000 - 33,000; Part time

Job summary

AUMOVIO is seeking a highly motivated Embedded Resilient AI Engineer Intern to support the evaluation, optimization, and deployment of AI models on resource-constrained embedded platforms, including FPGA-based systems and state-of-the-art MCUs.

The intern will work at the intersection of AI performance evaluation, embedded system design, and hardware-aware AI optimization, with a strong emphasis on robustness, efficiency, and deployability for automotive embedded use cases.

Qualifications

• Bachelor’s in Electrical Engineering, Computer Engineering, Computer Science, or related field.
• Programming experience in C/C++, Verilog/VHDL, Python is a plus.
• Basic knowledge of FPGA-based AI acceleration.
• Basic understanding of machine learning and embedded systems.
• Strong analytical and documentation skills.

Responsibilities

• Evaluate performance of AI/ML models under embedded constraints (latency, memory, power, accuracy).
• Propose and implement embedded AI solutions suitable for FPGA or MCU platforms.
• Deploy and benchmark AI inference on FPGA-based systems and/or up-to-date MCUs.
• Analyze trade-offs between different hardware platforms and optimization approaches.
• Document results and provide clear technical summaries.

Skills

C/C++

Python

Verilog/VHDL

Embedded systems

Analytical & docs

Education

Bachelor’s in Electrical Engineering, Computer Engineering, Computer Science, or related field

Tools

FPGA-based AI acceleration

MCU platforms

Job description

AUMOVIO is seeking a highly motivated Embedded Resilient AI Engineer Intern to support the evaluation, optimization, and deployment of AI models on resource-constrained embedded platforms, including FPGA-based systems and state-of-the-art MCUs.

The intern will work at the intersection of AI performance evaluation, embedded system design, and hardware-aware AI optimization, with a strong emphasis on robustness, efficiency, and deployability for automotive embedded use cases.