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

Are you a builder at the frontier of Edge AI/ML, eager to apply your academic research to high-impact, real-world problems? Do you thrive in ambiguous spaces where data, silicon, and algorithms intersect? Cirrus Venture Labs (CVL) may be the place for you.   CVL is Cirrus Logic’s technology accelerator, chartered to develop disruptive, scalable, and monetizable innovations. Our vision is to be a globally recognized innovation engine that shapes semiconductor markets by embedding intelligence directly where signals originate—across Voice, Sense, and Control domains.   As an ML Research & Systems Intern, you will work alongside our seasoned engineers to turn ambitious hypotheses into validated prototypes. This is not a "shadowing" role; you will be hands-on, contributing to our prototyping pipelines, data curation, and model optimization for deployment on constrained Edge and mixed-signal systems.

Responsibilites

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 Skills and Qualifications

Educational Background: 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.

Technical Depth: Strong foundational understanding of CNNs, RNNs (LSTMs), or Transformer-based architectures.

Programming Proficiency: Hands-on experience with Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX).

Signal Processing Exposure: Basic familiarity with processing time-series, audio, or sensor data.

Curiosity & Adaptability: Eager to work in a high-ambiguity "startup" environment within a larger corporation.

Communication: Ability to explain complex technical findings to a multi-disciplinary team of hardware and software engineers.

Preferred Skills and Qualifications

Embedded Interest: Experience or coursework in C/C++ or working with resource-constrained hardware (e.g., Raspberry Pi, ESP32, ARM Cortex-M).

Optimization Tools: Familiarity with TFLite, ONNX, or similar edge deployment toolchains.

Research Record: Previous research experience or publications in areas like anomaly detection, reinforcement learning, or generative AI for signal processing.

Open Source: Contributions to open-source ML projects or active participation in academic labs.