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Edge AI Engineer for Embedded ML & Inference
- San Jose, Arizona, United States
- San Jose, Arizona, United States
Über
If you are passionate about cutting-edge technology and thrive in a fast-paced, collaborative environment, TetraMem is the place for you. Join our global team to shape the future of AI computations and sustainable technology solutions while working at the forefront of innovation. Together, we can make a lasting impact. Are you ready for new challenges and new opportunities? Join our team! Current job opportunities are posted here as they become available. Subscribe to our RSS feeds to receive instant updates as new positions become available. 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. Salary Range: $110,000 - $300,000 / year
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- English
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