Senior robotics navigation engineer

We are looking for a Senior Robotics Navigation Engineer to own the localization, mapping, and navigation stack for our humanoid robots. You will design and implement 3D SLAM pipelines, multi-modal state estimation systems, and real-time navigation algorithms that enable our robots to understand where they are, build accurate maps of their environment, and move through it reliably.

This is a production-focused role. You are not prototyping algorithms in simulation — you are deploying them on hardware, validating them in real environments, and owning their reliability at scale. We want engineers who have closed that loop before: on self-driving cars, AGVs, mobile robots, or similar deployed autonomous systems.

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  Design, implement, and deploy production-grade 3D SLAM and localization systems fusing data from LiDAR, RGB-D cameras, IMUs, wheel encoders, and proprioceptive signals

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  Build and maintain state estimation pipelines — Kalman Filters, Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), or Factor Graph backends (GTSAM, Ceres, g2o) — with reliable accuracy in dynamic, GPS-denied, and perceptually degraded environments

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  Develop real-time 3D navigation algorithms: costmap generation from point clouds, global and local path planners (sampling-based, optimization-based), and traversability analysis

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  Implement sensor calibration pipelines (intrinsic and extrinsic) for multi-sensor rigs; own the calibration quality that underpins system accuracy

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  Design and build the evaluation and regression frameworks that prove the navigation stack is working correctly — logging, metrics, replay tooling, and failure analysis infrastructure

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  Collaborate with perception, controls, and hardware teams to integrate the navigation stack end-to-end into the full robot autonomy system

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  Troubleshoot and resolve challenging field failures; own root cause analysis and system-level fixes when localization or navigation breaks in deployment

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  Mentor junior engineers and contribute to technical roadmap planning for the autonomy stack

• 5+ years of industry experience in robotics autonomy, with a primary focus on SLAM, localization, or state estimation

• Proven track record of deploying navigation or SLAM systems on real autonomous platforms — self-driving vehicles, AGVs, mobile robots, or equivalent — not just simulation or research prototypes

• Deep theoretical and practical command of probabilistic robotics: Bayesian filtering, sensor fusion, covariance modeling, and non-linear optimization

• Hands-on experience with 3D SLAM modalities: LiDAR SLAM, Visual SLAM (VSLAM), Visual-Inertial Odometry (VIO), or multi-modal fusion

• Proficiency in C++ (C++14/17 or newer) for real-time, performance-critical code; strong software engineering fundamentals

• Experience with optimization libraries: GTSAM, Ceres Solver, g2o, or equivalent factor graph backends

• Familiarity with ROS/ROS 2 and standard robotics tooling

• Ability to explain why a localization module failed in a specific scenario to both a technical peer and a non-technical stakeholder

• M.S. or Ph.D. in Robotics, Computer Science, Electrical Engineering, or related field

• Experience with learning-based or hybrid approaches to localization and mapping (e.g., neural implicit maps, foundation model-assisted SLAM)

• Background in semantic SLAM or scene understanding — associating geometric maps with object-level semantics

• Experience with GPU acceleration (CUDA) for perception or navigation pipelines

• GNSS/RTK fusion experience for outdoor or GPS-blended deployments

• Familiarity with map management at scale: map storage, versioning, sharing across a robot fleet, and lifecycle management

• Prior experience in a startup or fast-moving R&D environment with an emphasis on shipping

• Contributions to open-source SLAM or navigation frameworks (ORB-SLAM, RTAB-Map, Cartographer, LIO-SAM, KISS-ICP, etc.)