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.
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Position Overview We're looking for an engineer to own the spatial foundations of our autonomy stack: how our sensors relate to each other and to the vehicle, and how that information is fused into a continuous, trustworthy estimate of where the vehicle is. The environments our trucks operate in, including port terminals, container yards, warehouses, and the corridors that connect them, all present unique challenges to both. This is a role for someone who thinks about sensor geometry and state estimation as facets of the same problem.
Key Responsibilities
- Lead the extrinsic calibration of our sensor suite and the localization algorithms that consume its outputs, ensuring both are robust, well-characterized, and evolve together
- Develop evaluation frameworks and metrics that let us measure calibration and localization quality honestly across fleet data
- Debug failures in real-world logs and translate findings into algorithmic improvements
- Collaborate with Autonomy, vehicle software, fleet operations, and data teams to tighten the loop between these foundational systems and their downstream consumers
- Contribute to the broader shape of how Humble approaches sensor geometry and state estimation as the team and fleet grow
Minimum Qualifications
- BS, MS, or PhD in Robotics, Computer Science, or a related field — or equivalent industry experience
- Strong foundation in the geometry, state estimation, and optimization methods: nonlinear optimization, Kalman filtering, factor graphs, sensor modeling, and similar
- Hands-on experience contributing to production-grade calibration and/or localization systems
- Proficiency in Python and Rust for developing algorithms and the tooling around them
- Comfortable reasoning about uncertainty — both characterizing outputs and tracing propagation to downstream systems
- Eligible to work in the United States
Preferred Qualifications
- Experience with online or continuous calibration, where calibration updates feed live into localization
- Experience building evaluation frameworks at fleet scale, outside controlled lab environments
- Familiarity with the failure modes of INS/GNSS in challenging environments, and techniques for handling them
- Comfort with modern build and dev environments (Bazel, monorepos, dev containers, or similar)
- Comfort operating as an early team member — high ownership, low ego, fast iteration
CompensationThis 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.
Additional Information
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.
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