Location
markham
Posted
June 01, 2026
Commute
Local Area
Local Opportunity Near You!
This job is in your area. Enjoy a short commute and work close to home.
Job Description
Job Overview
We are seeking a Machine Learning software engineer with embedded experience to develop and deliver novel embedded AI solutions for infotainment and ADAS/Autonomous Driving. Key Responsibilities
Design and implement core components of the ML runtime framework for inference on embedded systems. Collaborate with compiler, hardware, and model teams to co-design efficient execution paths for AI workloads. Develop and maintain C++ code for runtime kernels and system-level integration. Develop tools to assist with performance profiling and debugging of quantized model accuracy. Analyze and improve runtime behavior using profiling tools and hardware counters. Support deployment of models from popular ML frameworks (Onnx, TensorFlow, PyTorch) onto Qualcomm’s inference stack. Challenge the status quo and drive innovations to be best-of-class. Required Skills & Experience
Strong hands‑on experience in performance optimization for embedded or low‑power syst...
We are seeking a Machine Learning software engineer with embedded experience to develop and deliver novel embedded AI solutions for infotainment and ADAS/Autonomous Driving. Key Responsibilities
Design and implement core components of the ML runtime framework for inference on embedded systems. Collaborate with compiler, hardware, and model teams to co-design efficient execution paths for AI workloads. Develop and maintain C++ code for runtime kernels and system-level integration. Develop tools to assist with performance profiling and debugging of quantized model accuracy. Analyze and improve runtime behavior using profiling tools and hardware counters. Support deployment of models from popular ML frameworks (Onnx, TensorFlow, PyTorch) onto Qualcomm’s inference stack. Challenge the status quo and drive innovations to be best-of-class. Required Skills & Experience
Strong hands‑on experience in performance optimization for embedded or low‑power syst...