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# Data Engineer

- **Company:** HUMBLE ROBOTICS COMPANY
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Amazon Web Services, Batch Processing, Cloud Database, Databases, Software Debugging, Python (Programming Language), Machine Learning, Metadata Repositories, Data Streaming, Feature Store, Information Technology, Lidar, Serverless Computing
- **Published:** October 23, 2026
- **Apply:** https://www.thejobnetwork.com/job/5b8805e5-60ec-4ac1-8e56-a586fe54b174/data-engineer

## About the Role

* BS, MS, or PhD in Computer Science, Engineering, Robotics, or a related field - or equivalent industry experience
 * Strong proficiency in Python, including writing maintainable code that other engineers will read and extend
 * Solid database fundamentals and an intuition for designing schemas that hold up as requirements evolve
 * Working understanding of how ML training pipelines consume data, and an eye for designing upstream systems that serve them well
 * Comfortable working in large codebases and modern build/dev environments (Bazel, monorepos, dev containers, or similar)
 * Curious, flexible, and pragmatic - able to pick up unfamiliar tools and reason from first principles rather than relying on prior recipes
 * Eligible to work in the United States

Preferred Qualifications

 * Experience working with data in an autonomous vehicle, robotics, or similar context
 * Familiarity with Foxglove, rerun, or similar visualization/data-platform tooling
 * Experience designing or maintaining data catalogs, metadata stores, or feature stores
 * Background in handling high-volume multi-modal data (video, point clouds, time-series) at terabyte-plus scale
 * Cloud data engineering experience (GCP or AWS - object storage, serverless triggers, batch processing)
 * Comfort operating as an early team member - high ownership, low ego, fast iteration

## Description

* Build and maintain pipelines that ingest, validate, and process multi-modal sensor logs from our vehicles
 * Design schemas and data models that make our driving data discoverable and queryable for ML training, evaluation, and debugging
 * Turn raw driving data into the derived signals, annotations, and aggregates that downstream teams consume
 * Write tooling that the broader team relies on day-to-day: data loaders, query interfaces, dataset assembly utilities
 * Collaborate closely with ML, vehicle software, curation, and fleet operations to make sure data flows smoothly from collection through to model training
 * Contribute to the design of our data stack, making decisions that scale with the team and the fleet