Calibration and Localization Engineer

Reposted 20 Days Ago
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San Francisco, CA, USA
In-Office
Mid level
Artificial Intelligence • Logistics • Robotics • Transportation
The Role
Own and improve sensor extrinsic calibration and localization systems for autonomous trucks. Develop evaluation metrics and fleet-scale frameworks, debug real-world failures, and collaborate across autonomy, vehicle software, and data teams to tighten sensor geometry and state-estimation pipelines.
Summary Generated by Built In
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 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

Compensation

    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.

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 **[email protected]** 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.

Skills Required

  • BS, MS, or PhD in Robotics, Computer Science, or related field, or equivalent industry experience
  • Strong foundation in geometry, state estimation, and optimization methods (nonlinear optimization, Kalman filtering, factor graphs, sensor modeling)
  • Hands-on experience contributing to production-grade calibration and/or localization systems
  • Proficiency in Python and Rust
  • Comfortable reasoning about uncertainty and propagation to downstream systems
  • Eligible to work in the United States
  • Experience with online or continuous calibration where updates feed live into localization
  • Experience building evaluation frameworks at fleet scale outside lab environments
  • Familiarity with INS/GNSS failure modes and mitigation techniques
  • Comfort with modern build and dev environments (Bazel, monorepos, dev containers, or similar)
  • Comfort operating as an early team member with high ownership and fast iteration
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The Company
29 Employees
Year Founded: 2026

What We Do

Humble Robotics develops autonomous, electric Class 8 haulers designed for efficient and cost-effective commercial freight transportation. Their purpose-built, cabless vehicles blend vision-language-action models with lightweight hardware on a universal platform. By reimagining ground transportation with advanced physical AI, the company aims to structurally lower freight costs, reduce emissions, and provide a complete dock-to-dock solution for global logistics networks.

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