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# Staff Tech Lead, Machine Learning Engineer, Perception

- **Company:** Waymo LLC
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $251,000.0 - $310,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Computer Vision, C++ (Programming Language), Python (Programming Language), Machine Learning, Object Detection, Rapid Prototyping Process, Tensorflow, Sensor Fusion, Software Engineering, Pytorch, Deep Learning, Information Technology
- **Published:** August 30, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/85695921/1

## About the Role

* Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience
 * 7+ years experience in Machine Learning and/or Computer Vision
 * Strong expertise in Python and C++ and a solid software engineering background
 * Experience with ML frameworks like PyTorch, JAX, or Tensorflow

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

## Description

* Lead the design and development of multi-sensor, multi-task model architectures for dense and complete scene understanding with a focus on key tasks including object detection, occupancy prediction, road understanding, and trajectory planning.
 * Architect and develop the foundational modeling libraries and software powering model development and experimentation.
 * Design and implement scalable core model components, including sensor encoders, sensor fusion modules, backbones, and temporal aggregation / memory blocks.
 * Adapt SOTA ML technologies to unique compute requirements, leading optimizations for latency, throughput, and memory to operate efficiently within strict compute budgets.
 * Drive rapid prototyping by building and maintaining flexible pipelines and development environments for fast iteration on novel model designs, enabling seamless integration of field advancements and in-depth analysis of model training dynamics and learned representations.


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