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# Senior Embedded Software Engineer

- **Company:** Toumetis
- **Location:** Stoke Gifford, UK
- **Experience:** Expert
- **Salary:** £60,000.0 - £70,000.0
- **Contract:** Permanent contract
- **Skills:** Adobe Flash, Artificial Intelligence, ARM Architecture, C++ (Programming Language), Program Optimization, Computer Engineering, Embedded Software, Firmware, Python (Programming Language), Real-Time Operating Systems, Tensorflow, Reduced Instruction Set Computing, Signal Processing, PIC Microcontroller, Pytorch, Hardware Infrastructure
- **Published:** September 10, 2026
- **Apply:** https://www.collegerecruiter.com/job/2860992613-senior-embedded-software-engineer

## About the Role

* Languages: Expert proficiency in C/C++/Rust (for the device) and Python (for the model pipeline).
 * Frameworks: Deep experience with TinyML tools (TFLite Micro, CMSIS-NN, or STM32Cube.AI).
 * Hardware Knowledge: Familiarity with ARM Cortex-M series or RISC-V architectures.
 * Mathematical Foundation: Strong understanding of linear algebra and signal processing (FFTs, digital filters).

## Description

Working alongside our Data Science team you will be responsible for deploying AI algorithms to edge devices like Smart Meters, acting as a bridge between high level data science and low level hardware engineering.

You will be responsible for taking trained models and "shrinking" them to fit within the kilobyte-range memory and milliwatt power budgets of our hardware. Your work will enable real-time anomaly detection, load forecasting, and grid health monitoring directly at the point of consumption., * Model Optimization: Convert high-level Python/TensorFlow/PyTorch models into optimized C++, Rust or Flatbuffer formats using TensorFlow Lite for Microcontrollers or Edge Impulse.

 * Resource Management: Implement techniques like 8-bit quantization, weight pruning, and knowledge distillation to ensure models run within <256KB of RAM and minimal Flash storage.
 * Firmware Integration: Collaborate with Embedded Engineers to integrate inference engines into the device's firmware (RTOS-based) without disrupting primary metrology functions.
 * Signal Pre-processing: Develop efficient Digital Signal Processing (DSP) pipelines to clean raw voltage/current data before it reaches the AI model.


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