Machine Learning Engineer

Embedded ML Engineer – TinyML for Edge Devices

Embedded Systems | TinyML | Sensor Intelligence

What You’ll Do

• Design, deploy, and optimize TinyML models on microcontrollers (ARM Cortex-M, STM32, nRF52)

• Use tools such as TensorFlow Lite for Microcontrollers (TFLM) and CMSIS-NN for efficient on-device inference

• Lead data acquisition, signal preprocessing, and feature extraction on embedded platforms (e.g., motion, audio)

• Profile and optimize systems for real-time performance and ultra-low power consumption

• Collaborate with firmware, hardware, and ML teams to integrate models into end-user products

• Evaluate new techniques and tools in the fast-evolving TinyML ecosystem

Your Background

• Practical experience with TinyML toolchains: TFLM, Edge Impulse, or uTensor

• Strong skills in C/C++ development for embedded systems

• Proficient in working with bare-metal or RTOS-based environments

• Hands-on experience with real-time signal processing and low-power design

• Familiarity with embedded development tools (e.g., GCC, Keil, Segger, or IAR)

• Experience profiling performance and optimizing inference latency and memory footprint

Preferred Qualifications

• Experience working with BLE SoCs or sensor-rich IoT devices

• Familiarity with ARM’s CMSIS-DSP and hardware accelerators

• Background in audio or motion-based machine learning

• Contributions to open-source TinyML or embedded AI libraries

• MSc or equivalent in Embedded Systems, Computer Engineering, or Applied AI

If you're passionate about building intelligent, energy-efficient embedded systems, and want to work in a collaborative, forward-thinking environment, we’d love to hear from you!