Embedded AI/ML Software Engineer

Job-ID27922404Reference26-09922 JOB DESCRIPTION 100% Onsite 105 Commerce Valley Drive West Suite 100 Markham, Ontario L3T 7W3 Top 5 Required Skills 1. Develop and maintain C/C code for runtime and system-level integration. 2. Develop tools to assist with debugging and improving code quality 3. Strong hands-on experience in performance optimization for embedded or low-power systems. 4. Proficient in C/C programming, with a focus on system-level and runtime development. 5. Strong understanding of embedded system design, with emphasis on memory hierarchy and hardware–software interaction. General Summary: As part of the Low Power AI Solutions team, you will contribute to enabling efficient AI deployment on low‐power accelerators. This role focuses on developing and optimizing the infrastructure that supports machine learning inference on embedded edge devices. You will implement performance‐critical components, apply advanced optimization techniques, and extend runtime support for widely used ML architectures optimized for low‐power hardware. Your work will have a direct impact on the performance, latency, and power efficiency of AI applications running on platforms. Key Responsibilities: Design and implement components of the ML framework for inference on embedded systems. Collaborate with multiple development teams to co‐design efficient execution paths for AI workloads Analyze and improve runtime behavior using profiling tools. Support deployment of models from popular ML frameworks (e.g., Onnx, TensorFlow, PyTorch) onto inference stack. Required Skills & Experience: Strong hands-on experience in performance optimization for embedded or low-power systems. Proficient in C/C programming, with a focus on system-level and runtime development. Strong understanding of embedded system design, with emphasis on memory hierarchy and hardware–software interaction. Experience with Linux/Android development environments and toolchains. Familiarity with computer architecture, especially for AI accelerators or DSPs. Basic knowledge of machine learning concepts and model structures. Preferred Qualifications: Master’s degree in Computer Science, Engineering, or related field. 2 years of experience with ML frameworks (e.g., TensorFlow, PyTorch, ONNX). 3 years of experience in embedded system development and optimization for ML inference. 3 years of experience with C/C in performance-critical environments. Experience with low-level OS interactions (Linux, Android, QNX). Familiarity with quantization, graph optimization, and model deployment pipelines. Experience working in cross-functional teams and large matrixed organizations. Minimum Qualifications: Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 3 years of Software Engineering, Systems Engineering, or related work experience. Master's degree in Computer Science, Engineering, Information Systems, or related field and 1 year of Software Engineering, Systems Engineering, or related work experience.