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# Perception Machine Learning Engineer

- **Company:** Waymo LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $170,000.0 - $216,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Big Data, C++ (Programming Language), Python (Programming Language), Machine Learning, Object Detection, Tensorflow, Sensor Fusion, Pytorch, Deep Learning, Model Validation, Information Technology
- **Published:** July 2, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/15064227?backUrl=%2Fcareer%2F15064227%2FMachine-Learning-Engineer-Perception-Modeling-California-San-Francisco

## About the Role

* Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience
 * 3+ years experience in Machine Learning and/or Computer Vision
 * Experience with Python
 * Experience with ML frameworks like PyTorch or JAX

We prefer:

 * MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline
 * Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI
 * Experience with C++

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.

## Description

In this hybrid role you will report to a Technical Lead Manager.

You will:

 * Apply machine learning techniques to build multi-modal sensor fusion architectures and spatial-temporal representation learners for object detection and tracking, occupancy and semantic segmentation, road understanding, etc.
 * Develop scalable recipes for large data, large model training running on Alphabet's compute infrastructure, create methods and recipes for pre-training and post-training.
 * Develop methods and recipes for distributed fine-tuning enabling multiple developers to simultaneously improve the model, develop methods and recipes to avoid regression against a production system.
 * Develop and maintain model evaluation recipes and metrics for measuring and improving performance of pre-trained and fine-tuned models


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