Dexmate
Senior robotics navigation engineer
Santa Clara Office · Senior
Sponsorship not specifiedDetected 184 days ago
C++AlgorithmsRoadmappingRoboticsElectrical EngineeringSensorsResearch
About the 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.
Responsibilities
- Design, implement, and deploy production-grade 3D SLAM and localization systems fusing data from LiDAR, RGB-D cameras, IMUs, wheel encoders, and proprioceptive signals
- 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
- Develop real-time 3D navigation algorithms: costmap generation from point clouds, global and local path planners (sampling-based, optimization-based), and traversability analysis
- Implement sensor calibration pipelines (intrinsic and extrinsic) for multi-sensor rigs
- own the calibration quality that underpins system accuracy
- Design and build the evaluation and regression frameworks that prove the navigation stack is working correctly - logging, metrics, replay tooling, and failure analysis infrastructure
- Collaborate with perception, controls, and hardware teams to integrate the navigation stack end-to-end into the full robot autonomy system
- own root cause analysis and system-level fixes when localization or navigation breaks in deployment
- Implement sensor calibration pipelines (intrinsic and extrinsic) for multi-sensor rigs; own the calibration quality that underpins system accuracy
Requirements
- 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
- 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
- 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
Nice to have
- M.S. or Ph.D. in Robotics, Computer Science, Electrical Engineering, or related field
- 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.)
Skills
- Mentor junior engineers and contribute to technical roadmap planning for the autonomy stack
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