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 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.
- Ability to provide mentorship, technical guidance, and career development support to junior engineers and interns.
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.
Salary Range: $110,000 - $200,000 / year
