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# Software Engineer, Perception

- **Company:** Aurora Innovation, Inc.
- **Location:** United States
- **Experience:** Expert
- **Salary:** $162,000.0 - $260,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Big Data, C++ (Programming Language), Virtual Camera Systems, Python (Programming Language), Machine Learning, Object Detection, Tensorflow, Software Deployment, Software Engineering, Pytorch, Deep Learning, Information Technology, Machine Learning Operations, Lidar
- **Published:** July 17, 2026
- **Apply:** https://www.dice.com/job-detail/889d59b1-37ff-41c7-a5dd-7f321ad0cbc2

## About the Role

* BS, MS, or PhD in Computer Science, Robotics, Engineering, or a related field with a strong foundation in one or more focus areas of ML, including deep learning, computer vision, recursive state estimation, structured prediction, and optimization.
 * 6+ years of research-based or professional experience with C++ and Python.
 * Comprehensive grasp of linear algebra, discrete/continuous optimization, supervised/unsupervised methods, and generative/discriminative models.
 * Strong publication record at top-tier robotics/computer vision conferences/journals, or significant industry experience in relevant fields (robotics, computer vision, self-driving technology), * Experience utilizing deep learning frameworks such as PyTorch or TensorFlow.
 * Advanced production-level knowledge of C++ is highly preferred.
 * Prior experience applying computer vision and machine learning directly to complex robotics problems.
 * Experience deploying computer vision/ML models at scale across large physical fleets.
 * Proven ability to work effectively in environments requiring close cross-team collaboration.
 * Experience working with 3D data, including 3D object detection, tracking, semantic segmentation, or processing point clouds from LiDAR, Radar, and multi-camera system

## Description

* Tackle End-to-End Autonomy Challenges: Address object detection, tracking of traffic actors, action recognition, and semantic understanding of diverse traffic scenes.
 * Deploy Production-Ready Software: Guide software development from initial prototype to production deployment on a real-time AV platform, leveraging large-scale data sets for training and analysis.
 * Collaborate and Iterate: Partner with team members to diagnose, analyze, and resolve failure modes encountered during on-road and simulated testing to produce robust, well-tested systems.
 * Scale ML Engineering Pipelines: Build and scale robust ML pipelines to facilitate quick experimentation as well as large-scale training and testing.


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