Machine Learning Intern - KWS/AED
Summary Description:
Syntiant Corp., a leader in the high-growth AI software and semiconductor solutions space, is looking for a Machine Learning Intern to take on a critical role supporting our Algorithms team's work on keyword spotting (KWS) and audio event detection (AED) models deployed on ultra-low-power edge hardware.
The Machine Learning Intern will work alongside senior ML engineers to help build, evaluate, and improve deep learning models that run directly on Syntiant's NDP-class neural decision processors — models that must detect wake words, spoken commands, and acoustic events (e.g., glass breaking, alarms, sirens) in real time under extremely tight memory and power budgets. This role spans the full modeling pipeline, from signal processing and data curation through architecture design, training, and evaluation against hardware constraints.
Requirements
Specific Duties and Responsibilities:
- Support development and evaluation of KWS and AED models, including single-stage and cascaded (multi-stage gate/verifier) detection architectures.
- Assist with audio pipeline and feature extraction work — filterbank design, log-mel and PCEN-based frontends, and diagnosing numerical or performance issues in training/eval pipelines.
- Help design and prune CNN architectures to fit hardware constrain
