About the job
About Us
Nuro is at the forefront of self-driving technology, striving to make autonomous driving accessible to everyone. Established in 2016, we are dedicated to developing the world’s most scalable driver by merging advanced artificial intelligence with automotive-grade hardware. Our flagship technology, the Nuro Driver™, is licensed across various applications, including robotaxis, commercial fleets, and personal vehicles. With proven technology from years of successful self-driving deployments, we provide automakers and mobility platforms with a clear pathway to commercial-scale autonomous vehicles, paving the way for a safer, more connected future.
Role Overview
At Nuro, we prioritize a machine-learning-centric approach to autonomous driving. The performance of our systems relies significantly on the volume and diversity of training and evaluation data. Our team is vital in enhancing autonomous driving systems by ensuring high-quality labeled data accessibility. This is achieved through a sophisticated labeling stack that includes a workflow execution framework, supporting infrastructure, and an array of data annotation tools. Our autonomy stack is powered by an industry-leading sensor suite, capable of efficiently processing and annotating millions of sensor data points weekly.
The platform team's mission is to ensure that labeled data is readily available for all users. Our systems must be dependable and scalable, encompassing everything from request submissions to progress tracking, data dumping, and model development. We work closely with autonomy engineers to guarantee the labeled data's quality and comprehensiveness. As Nuro expands its services, it becomes increasingly critical to capture and promptly triage all issues.
Key Responsibilities
- Develop and maintain highly available and fault-tolerant systems for data annotation.
- Productionize core infrastructure for our cutting-edge autonomy system.
- Enhance label quality through data-driven metrics and monitoring.
- Leverage state-of-the-art ML research to automate and optimize the data labeling lifecycle.
Qualifications
- Bachelor's or Master's degree in a relevant field with 5+ years of industry experience.
- Proven experience in designing, building, and operating highly scalable and reliable distributed data systems.
- Demonstrated ability to lead cross-functional projects with strong communication skills.
- Expertise in data processing and machine learning techniques.