Embedded AI Engineer - #1173462

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. This role sits at the intersection of AI, embedded systems, and silicon — converting trained models into highly efficient on-device inference pipelines for battery-powered and always-on edge applications across vision, audio, activity, and time-series domains.

Responsibilities

• Deploy AI models onto resource-constrained, proprietary SoC designs

• Optimize models across memory, power, and latency dimensions

• Execute model quantization at INT8 or lower precision

• Map neural networks to microcontrollers, DSPs, and AI accelerators

• Build inference pipelines in bare metal or Zephyr OS (advantageous)

• Validate embedded inference outputs against Python reference models

• Diagnose and resolve numerical mismatches between floating-point and quantized inference

• Tune memory layout, buffer reuse strategies, and tensor ordering

• Work closely with hardware and firmware engineering teams

Requirements

• Solid grounding in machine learning fundamentals

• Practical experience deploying AI on embedded or edge hardware

• Strong proficiency in C/C++ (embedded context) and Python

• Hands-on experience with quantization techniques — PTQ or QAT

• Familiarity with memory-constrained system design

• Demonstrated ability to optimize for latency, memory footprint, and power draw

Bonus Skills

• Exposure to TensorFlow Lite Micro, TVM, CMSIS-NN, or ONNX Runtime

• Knowledge of RISC-V architecture or custom AI accelerator design

• Experience with edge sensors — audio, IMU, vision, or wearables

• Background in power profiling and power optimization workflows

• Agile development exposure

Why Join

• Work directly on production silicon and real edge-AI systems

• Build AI designed to run for months or years on battery power

• Direct collaboration with chip architects and systems engineers

• End-to-end ownership from model training to silicon deployment

• High-impact position within a fast-moving deep-tech environment