Machine Learning Engineer

• Identify opportunities for new DSP/ML algorithms by understanding device constraints, sensor characteristics, and hardware capabilities
• Develop audio and multimodal ML models for embedded and edge AI applications
• Design, train, evaluate, and iterate on models for real-world sensing and interaction use cases
• Prototype novel approaches for low-latency, on-device audio and multimodal processing
• Develop software for RTOS environments such as FreeRTOS
• Deploy models to device runtimes and hardware accelerators including DSP, NPU, and MCU
• Convert trained ML models into efficient embedded implementations using C/C++, quantization, and fixed-point inference
• Optimize memory footprint, SRAM usage, latency, and power consumption
• Integrate ML inference into real-time firmware pipelines
• Design end-to-end embedded AI systems from sensor through preprocessing, model, and postprocessing
• Architect reusable embedded ML platform components across multiple hardware targets
• Profile and optimize performance on MCUs, DSP cores, NPUs, and custom accelerators
• Work with cross-compilation toolchains, CMake-based builds, and modular codebases
• Integrate with on-device ML runtimes such as TFLite Micro, ExecuTorch, and custom interpreters
• Provide feedback on model architecture for deployment efficiency and real-time behavior
• Collaborate with ML researchers, DSP experts, firmware engineers, and hardware teams

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