Opportunities in San Francisco, United States

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.

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.

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

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

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

This role is eligible for base salary + benefits + equity compensation. Salary ranges are determined by role, level, and location. Within the range, individual pay is determined by additional factors, including qualifications, skills, experience, and location.

As part of the interview process, we may use Artificial Intelligence (AI) tools to compare your qualifications and experience to the job description. A human reviews all AI output and makes a final hiring decision. Humble Robotics does not rely on the output to make any employment decisions. Some applicants may have a legal right to opt-out of the use of AI as part of our interview process. Contact legal@humblerobotics.ai to exercise this right or if you have further questions on the use of AI tools in our hiring process. Humble Robotics is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, marital status or any other characteristics protected by law. Humble Robotics will consider qualified applicants with arrest and conviction records in a manner consistent with local ordinances.