Machine Learning Intern - KWS/AED

Syntiant • Redwood City, CA

1d

About The Position

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

• Candidate pursuing a Bachelor's or Master's degree in Computer Science, Electrical Engineering, Machine Learning, or a related field, with hands-on experience in deep learning for audio or speech (coursework, research, or project experience with CNNs/RNNs on spectrogram or time-series audio data).
• Proficiency in Python and a deep learning framework (TensorFlow/Keras preferred; PyTorch acceptable).
• Familiarity with audio signal processing fundamentals (spectrograms, mel filterbanks, feature extraction).
• Understanding of standard ML evaluation concepts (precision/recall trade-offs, ROC/DET curves, confusion analysis) — bonus if applied to detection/verification tasks rather than pure classification.
• Strong analytical mindset, comfort working with messy real-world data, and clear written communication for summarizing experimental results.

Nice To Haves

• Exposure to model efficiency concepts (quantization, parameter budgets, edge/embedded ML constraints) is a strong plus, though not required.
• Prior internship, research, or personal project experience in audio ML, KWS, or acoustic event detection is a plus but not required.

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 constraints (fixed input shapes, 8-bit quantization, limited parameter budgets, restricted op sets such as depthwise separable convolutions with hardware-supported stride/pooling operations).
• Build and run false-accept (FA) diagnostic tooling — categorized probe sets, confusion analysis, Grad-CAM/occlusion-style visualization to understand what a model is actually keying on.
• Contribute to hard-negative mining and data augmentation strategies (e.g., SNR-based background noise mixing, targeted negative class collection) to reduce false accepts across everyday household/environmental sounds.
• Help plan and track data collection efforts, including structuring datasets by acoustic category/spec and maintaining collection logs and inventories.
• Analyze model run results across experiment variants (architecture, data, frontend) and summarize findings for the team.
• Collaborate with ML, DSP, and hardware/firmware engineers to validate models against real deployment conditions.

What This Job Offers

• Career Level: Intern