Summer Intern, Embedded Machine Learning Engineer (AI/ML)

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.