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# Machine Learning Engineer, NVH

- **Company:** Tesla Motors
- **Location:** Fremont, CA, United States
- **Salary:** $124,000.0 - $210,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, C++ (Programming Language), Software Quality, Code Review, Computer Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, Signal Processing, Software Construction, Software Deployment, Data Ingestion, Pytorch, Deep Learning, Model Validation, Information Technology, Software Version Control
- **Published:** June 12, 2026
- **Apply:** https://diversityjobs.com/career/17240553/Machine-Learning-Engineer-Nvh-California-Fremont

## About the Role

* Degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related field, or equivalent experience
 * Strong 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 evaluation
 * Hands-on experience with neural network architectures for audio, including CNNs, RNNs, Transformers, and models tailored for event detection and classification
 * Proficiency with PyTorch or another major deep learning framework (e.g.,TensorFlow)

## Description

United States, California, Fremont
Jun 12, 2026
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 Do

 * Design, develop, and deploy machine learning models and audio algorithms for advanced NVH diagnostics across Tesla's vehicle and product lineup
 * Build and maintain end-to-end ML pipelines - from raw audio data ingestion and preprocessing to model training, evaluation, and production deployment
 * Collaborate cross-functionally with manufacturing, quality audit, and vehicle engineering teams to integrate diagnostic algorithms into Tesla's production lines
 * Drive continuous improvement of model performance through rigorous experimentation, iteration, and ablation studies
 * Develop tooling and frameworks to streamline data collection, labeling, and model monitoring in a production environment
 * Contribute to defining best practices in ML engineering, including code quality, testing, reproducibility, and documentation
 * Stay current with state-of-the-art research in audio ML, signal processing, and
 * deep learning; identify and apply relevant advancements to Tesla's challenges


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