Who We Are
Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides.
Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles.
With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected.
Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors.
About the Work
- Data Pipeline Architecture: Design and build scalable data ingestion and processing pipelines that turn data streams into targeted training datasets. Lead initiatives to improve data quality, detect anomalies, and manage out-of-distribution examples to ensure robust model training and deployment.
- Cross Functional Leadership: Work across autonomy teams and data infra teams to build effective ML data pipelines and products for ML engineers.
- ML Tooling & Introspection: Develop infrastructure and visualization tools that allow ML researchers to easily introspect data, identify model failure modes, query for new data samples, and understand data distribution shifts.
- Labeling Operations Integration: Collaborate closely with the data operations team to define quality standards, automate quality control (QC), and streamline the feedback loop between model performance and annotation guidelines.
- Active Learning & Data Mining Engines: Lead the engineering effort to operationalize research-grade active learning methods. E.g. build systems that compute embeddings or run inference at scale, manage vector databases, and automatically sample the most informative data points for labeling.