Data Engineer

About the role

About Humble Robotics

Working at Humble Robotics means taking on the biggest change in ground transportation in decades. We're building an autonomous, zero-emissions hauler that dramatically lowers the cost of freight with groundbreaking vision-based AI, designed for today's global logistics network.

We're a fast-moving, close-knit team of AV industry veterans and innovative thinkers. We don't believe culture can be engineered – but when it falls into place, it's a once-in-a-lifetime adventure.

Progress has never felt so present.

Position Overview We're looking for a data engineer to help us turn raw driving data into the well-structured, queryable, and trustworthy datasets that power our autonomy stack. You'll work across the full lifecycle of our data, from ingestion to the point it gets pulled into a training run, and your work will directly shape how quickly the rest of the team can iterate.

Key Responsibilities - 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

Minimum Qualifications - 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