Embedded AI Engineer
Summary
Builds, optimizes, and deploys neural networks on ultra-low-power, memory-constrained SoCs and AI accelerators — quantizing models to INT8, mapping them to microcontrollers/DSPs, and tuning memory, latency, and power for battery-powered edge devices. Core stack: C/C++, Python, TFLite Micro/TVM/CMSIS-NN.
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