Cirrus Logic

Spring Intern, Embedded Machine Learning Engineer – AI/ML

Cirrus Logic

internship

Posted on:

Location Type: Hybrid

Location: Austin • Texas • 🇺🇸 United States

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Job Level

Entry Level

Tech Stack

Open SourcePythonPyTorchTensorflow

About the role

  • 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.

Requirements

  • 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.
  • 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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
ML modelsmodel architecturesdata labeling strategiessynthetic data generationmodel compression techniquesquantizationpruningknowledge distillationCNNsRNNs
Soft skills
curiosityadaptabilitycommunication