Embedded AI Engineer

Company Description

Nanoveu Ltd (ASX: NVU) is an innovative applied technology company listed on the Australian Stock Exchange. EMASS is a Nanoveu's wholly owned subsidiary that is focused on developing SoC chips for Edge-AI at ultra-low power.

About the Role:

We are looking for an Embedded AI Engineer to build, optimize, and deploy neural networks on ultra-low-power, memory-constrained SoCs and AI accelerators. You will work at the intersection of AI, embedded systems, and silicon, translating trained models into highly efficient on-device inference pipelines for battery-powered and always-on edge applications for vision, audio, activity and time-series applications.

Roles and Responsibilites:

Deploy AI models on resource-constrained in-house design SoCs

Optimize models for memory, power, and latency

Perform model quantization (INT8 or lower)

Map neural networks onto microcontrollers, DSPs, and AI accelerators

Implement inference pipelines in bare metal or Zephyr OS (bonus)

Validate embedded inference against Python reference models

Debug numerical mismatches between float and quantized inference

Optimize memory layout, buffer reuse, and tensor ordering

Collaborate closely with hardware and firmware teams

Requirements:

Strong fundamentals in machine learning

Hands-on experience deploying AI on embedded or edge devices

Proficiency in C/C++ (embedded) and Python

Experience with quantization (PTQ or QAT)

Understanding of memory-constrained systems

Ability to optimize latency, memory footprint, and power consumption

Bonus Skills:

Experience with TensorFlow Lite Micro, TVM, CMSIS-NN, or ONNX Runtime

Knowledge of RISC-V architecture or custom AI accelerators

Experience with edge sensors (audio, IMU, vision, wearables)

Familiarity with power profiling and optimization

Agile development experience

Why Join Us:

Work on real silicon and production edge-AI systems

Build AI that runs for months or years on battery

Collaborate directly with chip architects and systems engineers

Own the full pipeline from model to silicon

High-impact role in a fast-moving deep-tech team