5+ years of industry experience or a PhD in Computer Science, Electrical Engineering, Machine Learning, or related fields.
Prior experience in a Team Lead role with strong technical leadership skills.
Hands-on experience in machine learning focused on edge AI and on-device inference.
Expertise in ML frameworks like PyTorch, TensorFlow (including TensorFlow Lite), and JAX.
Proficiency in Python and C/C++ with experience in ML model optimization.
Deep knowledge of model compression techniques such as quantization and pruning.
Experience with AI chip SDKs and hardware-specific toolchains.
Responsibilities
Develop, optimize, and deploy lightweight machine learning models for edge AI applications, especially in audio processing.
Implement and optimize ML models on embedded platforms like FPGA and custom ASIC solutions.
Collaborate with hardware and software teams to integrate ML models into production systems.
Research and apply state-of-the-art ML techniques to improve model efficiency and power consumption.
Enhance inference efficiency through model compression techniques like quantization and knowledge distillation.
Drive innovation by collaborating with cross-functional teams on system architecture.
Provide mentorship and technical leadership to junior engineers.
Benefits
Opportunity to publish research findings and present at conferences.
Engagement in open-source projects when applicable.
Collaborative work environment with cross-functional teams.
Potential for professional growth and leadership development.
Full Job Description
Responsibilities:
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 relevant industry experience (or a PhD) in Computer Science, Electrical Engineering, Machine Learning, or related fields.
Must have prior experience managing a team, serving in a Team Lead role, or demonstrating strong technical leadership and cross-functional coordination capabilities.
Strong hands-on experience in machine learning, with a focus on edge AI, on-device inference, and deploying lightweight models on resource-constrained devices.
Expertise in modern ML frameworks such as PyTorch, TensorFlow (including TensorFlow Lite), and JAX.
Proficiency in Python and C/C++, with practical experience in ML model optimization and production deployment.
Deep experience with model quantization (PTQ/QAT), pruning, knowledge distillation, sparsity, and other compression techniques for efficient edge inference.
Hands-on experience developing for or integrating with AI chip SDKs, neural accelerators (NPUs/DSPs), or hardware-specific toolchains (e.g., NVIDIA TensorRT, Qualcomm Neural Processing SDK, ARM Ethos, or similar).
Familiarity with edge inference runtimes (ONNX Runtime, ExecuTorch, TVM) and optimizing models for hardware constraints (latency, memory footprint, power consumption).
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.
