Staff Software Engineer, Behavior ML Data

Who We Are

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future.

About the Role

We are looking for a Senior/Staff Software Engineer to serve as a technical leader for Nuro’s ML Data engine. You will sit at the critical intersection of Autonomy, Machine Learning, and Infrastructure, acting as an architect for the systems that feed our autonomy AI models.

In this role you will be a member of the Autonomy team responsible for executing the technical strategy for transforming massive amounts of autonomy data into high-value training signals for autonomy decision making.  You will design and build data products for autonomy researchers, develop queries for rare "needle-in-a-haystack" scenarios, and trigger labeling and data ingestion workflows without human intervention.  You will partner directly with Autonomy ML researchers to understand their data needs, collaborate with infrastructure teams to define the right data interfaces and APIs, and build robust data selection, simulation, and introspection tools that can process data at scale. If you love solving challenging new problems with a mindset of deriving practical solutions to be used in the physical world, come join us

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 labeli