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Machine Learning Engineer, NVHTesla • Richmond Hill, Canada
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Machine Learning Engineer, NVH

Machine Learning Engineer, NVH

Tesla • Richmond Hill, Canada
14 days ago
Job type
  • Full-time
Job description
What To Expect Tesla's Noise, Vibration, and Harshness (NVH) team is seeking a passionate and skilled Machine Learning Engineer to help define the future of diagnostics for Tesla's products. You will work at the intersection of deep learning research and real-world manufacturing deployment, building systems that directly impact product quality and customer experience at scale.What You'll DoDesign, develop, and deploy machine learning models and audio algorithms for advanced NVH diagnostics across Tesla's vehicle and product lineupBuild and maintain end-to-end ML pipelines—from raw audio data ingestion and preprocessing to model training, evaluation, and production deploymentCollaborate cross-functionally with manufacturing, quality audit, and vehicle engineering teams to integrate diagnostic algorithms into Tesla's production linesDrive continuous improvement of model performance through rigorous experimentation, iteration, and ablation studiesDevelop tooling and frameworks to streamline data collection, labeling, and model monitoring in a production environmentContribute to defining best practices in ML engineering, including code quality, testing, reproducibility, and documentationStay current with state‑of‑the‑art research in audio ML, signal processing, and deep learning; identify and apply relevant advancements to Tesla's challengesWhat You'll BringDegree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related field, or equivalent experienceStrong proficiency in Python and C++, with demonstrated software engineering best practices (version control, testing, code review, etc.)Deep understanding of machine learning fundamentals: neural network architectures, loss functions, optimization strategies, regularization, and model evaluationHands‑on experience with neural network architectures for audio, including CNNs, RNNs, Transformers, and models tailored for event detection and classificationProficiency with PyTorch or another major deep learning framework (e.g., TensorFlow)#J-18808-Ljbffr
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Machine Learning Engineer, NVH • Richmond Hill, Canada

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