Summer Intern, Embedded Machine Learning… at Cirrus Logic · Austin

Summary: Cirrus Logic is a leading supplier of low-power, high-precision mixed-signal processing solutions for mobile and consumer applications. As an ML Research & Systems Intern, you will work alongside experienced engineers to develop prototypes in Edge AI/ML, focusing on model optimization and data curation for deployment on embedded systems. Responsibilities: - Rapid Prototyping: Assist in building and testing ML models for edge intelligence, specifically focusing on audio, sensor, and control signals - Data & Model Engineering: Support the design of model architectures and contribute to data labeling strategies, synthetic data generation, and augmentation pipelines - Edge Optimization: Explore and implement model compression techniques—such as quantization, pruning, and knowledge distillation—to ensure models run efficiently on embedded systems - Exploration & Benchmarking: Stay current on foundation/SLM trends and academic research; help define benchmarks and evaluation metrics to measure the success of CVL’s ML prototypes - Cross-Functional Collaboration: Partner with firmware, silicon, and systems engineers to understand the physical constraints of hardware and how they impact algorithmic accuracy Required Qualifications: - Currently enrolled in a Master's or Ph.D. program in Computer Science, Electrical Engineering, or a related field with a focus on ML/AI - Strong foundational understanding of CNNs, RNNs (LSTMs), or Transformer-based architectures - Hands-on experience with Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX) - Basic familiarity with processing time-series, audio, or sensor data - Eager to work in a high-ambiguity 'startup' environment within a larger corporation - Ability to explain complex technical findings to a multi-disciplinary team of hardware and software engineers Preferred Qualifications: - Experience or coursework in C/C++ or working with resource-constrained hardware (e.g., Raspberry Pi, ESP32, ARM Cortex-M) - Familiarity with TFLite, ONNX, or similar edge deployment toolchains - Previous research experience or publications in areas like anomaly detection, reinforcement learning, or generative AI for signal processing - Contributions to open-source ML projects or active participation in academic labs Required Skills: Machine Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Transformer Architectures, Python, PyTorch, TensorFlow, JAX, Signal Processing, Time-Series Data Processing, Audio Data Processing, Sensor Data Processing, Curiosity