Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
Work closely with hardware and software teams to integrate ML models into production systems.
Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
Provide technical leadership and mentorship to junior engineers.
Publish research findings, present at conferences, and contribute to open-source projects when applicable.
Requirements:
5 years of experience or PhD in Computer Science, Electrical Engineering, or related fields.
Strong experience in machine learning, with a focus on edge AI and lightweight model deployment.
Expertise in ML frameworks such as PyTorch, TensorFlow, JAX.
Proficiency in programming languages such as C/C , Python, and experience with ML model optimization.
Ability to work independently and collaboratively in a fast-paced startup environment.
Experience in one or more of the following areas considered a strong plus:
Understanding of ML compiler and runtime design.
Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.
Familiarity with hardware acceleration techniques.
Experience in embedded system development.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
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