We are seeking an Embedded AI Software Engineer to bridge the gap between high-level machine learning models and constrained hardware environments. In this role, you won't just be "using" AI; you will be responsible for the practical integration, optimisation, and deployment of signal processing and ML algorithms onto embedded SoCs.
Core Responsibilities
Algorithm Implementation: Implement sensor signal processing and ML algorithms across various embedded SoCs.
Optimization: Debug, verify, and tune C code derived from MATLAB or Python prototypes.
System Architecture: Navigate RTOS and multi-threaded environments to ensure seamless algorithm execution.
Hardware Prototyping: Deploy and test algorithms on reference hardware platforms and sensor systems.
Cross-Functional Collaboration: Support testing teams with deployment and document entire SW architectures and implementation flows.
Required Qualifications
C Mastery: Expert-level proficiency in C for embedded systems.
Bridge Building: Proven ability to implement algorithms originally coded in MATLAB or Python into efficient C.
Concurrency: Strong familiarity with RTOS and multi-threaded programming.
Analytical Mindset: Deep understanding of engineering trade-offs regarding power, memory, and complexity.
2+ years of embedded development focused on DSP or ML implementation. (Preferred)
Experience with fixed-point arithmetic and model quantisation. (Preferred)
Familiarity with ML frameworks (PyTorch, TensorFlow). (Preferred)
Knowledge of sensor hardware. (Preferred)
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