Mapping & Localization Engineer

Company background

Swarmbotics AI is a low-cost, swarm robotics company for industry and defense.  We see a world of ubiquitous low-cost robots transforming almost all aspects of society, but we see an urgent need in the defense industry.  We focus on building swarms of robots that incorporate a low-cost BOM, an autonomous stack optimized for off the shelf components, and a global planner that enables swarm capabilities for groups of robots to accomplish sophisticated tasks.  

Our first product is a defense application building Unmanned Ground Vehicles (UGVs), collectively termed - Attritable, Networked, Tactical Swarm (ANTS).  Each UGV in ANTS is an independently-tasked, attritable robot designed for on-demand and autonomous mobility.  When operating as a swarm, ANTS is capable of executing more advanced and coordinated, high-level capabilities across a battlespace.  ANTS will help solve some of the DoD’s biggest problems that will save lives and increase defense capabilities.

Stephen Houghton and Drew Watson are the Founders and have decades of experience in self-driving cars and trucks, humanoids, and UAVs with experience from NASA, JPL, Cruise, Embark, McKinsey, Amazon, and the CIA.  

Job description

Swarmbotics is on the forefront of deploying unmanned ground vehicle (UGV) swarms for defense applications and is seeking a highly skilled Simultaneous Localization & Mapping (SLAM) Engineer to join our team. This role is crucial in designing, implementing, testing, and maintaining the SLAM systems that enable our unmanned ground vehicles to navigate and map their environments accurately and, ultimately, in GPS denied/degraded areas. The ideal candidate will specialize in SLAM algorithms, sensor fusion, real-time localization, and mapping, ensuring our robotic swarms can operate effectively in diverse and challenging environments.  The selected candidate will have the opportunity to build a modern SLAM stack and apply that capability in a single-agent and collaborative, multi-agent context.

Required qualifications

• SLAM Development: Experience architecting and implementing robust SLAM algorithms to facilitate real-time localization and mapping for autonomous robotic systems.
• Sensor Fusion: Experience integrating and optimizing data from multiple sensors (e.g., LIDAR, cameras, IMUs) to enhance the accuracy and reliability of the SLAM system.
• Real-Time Processing: Proficiency in designing and implementing real-time processing pipelines that ensure low-latency, high-accuracy localization and mapping.
• Fault Management: Experience in designing systems for fault detection and recovery within SLAM, enhancing reliability under various operational conditions.
• Performance Optimization: Expertise in optimizing SLAM algorithms for computational efficiency, ensuring they can run on embedded systems with limited resources.
• Comfortable working in the high-paced, fluid environment of a tech startup.
• Excited to contribute to the defense of the United States and its allies.
• Must be eligible to work on export-controlled projects.

Preferred qualifications

• Experience with unmanned ground vehicles or robotic swarms.
• Experience with 3D SLAM and dense mapping techniques.
• Strong background in C++ and Python programming languages.
• ROS2 Integration: Experience developing and integrating SLAM systems within ROS2, ensuring seamless communication and operation across robotic subsystems.
• Familiarity with continuous integration/continuous deployment (CI/CD) pipelines.
• Proven track record of implementing SLAM systems in real-world robotics applications