ML Audio Engineer
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Before you apply
LocationTel Aviv, Israel (ZIL87)
Typefull-time
Company size10,000+ people
PostedOct 19, 2026 · 2w ago
Verified live5d ago
At a glanceSummarised by Seekless from the posting.
Design, train, and iterate on deep learning models for acoustic classification against noisy real-world audio
Enhance and optimize audio lineups for sound classification
Build and maintain the data pipeline: field recordings, augmentation, labeling, dataset versioning
Optimize models for edge deployment to meet latency and power budgets
MLOps stack: training infra, experiment tracking, reproducible pipelines, deployed-model monitoring
Skills
deep learning
audio signal processing
edge computing
Python
TensorFlow
PyTorch
MLOps
Company Overview
At Motorola Solutions, we believe that everything starts with our people. We’re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that’s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future.
Department Overview
We work at the intersection of deep learning, audio signal processing, and edge computing. You’ll join a small team who owns detection and classification models end-to-end — from data pipeline through training to optimized inference on embedded hardware.
Job Description
What you’ll do
•
Design, train, and iterate on deep learning models for acoustic classification against noisy real-world audio.
•
Enhance and optimize audio lineups for sound classification
•
Build and maintain the data pipeline: field recordings, augmentation, labeling, dataset versioning.
•
Optimize models for edge deployment to meet latency and power budgets.
•
MLOps stack: training infra, experiment tracking, reproducible pipelines, deployed-model monitoring.
Why this role
Small team, broad ownership, short loop between model and field. Super interesting job. Your work directly determines whether the system meets its targets.