Robotics Software Intern

Conduit's FactoryOS platform allows manufacturers to scale production ready automation solutions in days, not months. With a suite of powerful applications, we allow manufacturers to automate faster and smarter by coordinating every robot, machine, and system on one platform. At Conduit, our mission is to give every American manufacturer the power to automate their factories at lightning speed.

We’re already deployed in production at top manufacturers and growing fast. Backed by leading investors, and built by a team that moves at light speed with deep conviction in American industrial dominance.

We're seeking a talented Robotics Engineering Intern with a strong interest in SLAM, perception, and autonomous navigation. This is a paid internship role with the opportunity to work on real robotics systems in production environments, and a clear path to full-time employment upon graduation.

• Build robust localization and mapping systems. Develop and improve SLAM pipelines that enable robots to localize reliably and build accurate maps in real-world factory environments.
• Fuse sensor data into reliable state estimates. Work on perception and estimation pipelines that combine cameras, IMUs, and other onboard sensors for robust pose estimation and navigation.
• Improve autonomy in challenging environments. Tackle real-world problems such as dynamic lighting, motion blur, fast moving vehicles, and changing environments to make localization and mapping more reliable on hardware.
• Develop mapping and optimization systems. Contribute to pose graph optimization, filtering based methods, map maintenance, and dynamic mapping data structures that support long term robot operation.
• Prototype fast, deploy faster. Test algorithms directly on live robotic systems, evaluate performance with real sensor data, and iterate quickly based on what actually works on the factory floor.
• Debug across the full robotics stack. Troubleshoot software, hardware, and system level issues across sensing, calibration, perception, and navigation pipelines.
• Research and apply practical improvements. Explore online sensor calibration strategies, online map optimization techniques, and other methods that improve SLAM robustness in production settings.
• Explore and prototype learning based SLAM methods. Investigate data driven approaches that improve robustness across dynamic environments, challenging lighting conditions, fast motion, and other real-world failure cases.
• Collaborate across teams. Work closely with robotics, backend, and deployment teams to integrate perception and localization capabilities into larger robotic systems.

• Someone with strong proficiency in C and Python for robotics development.
• Comfortable with core SLAM and estimation concepts such as pose graph optimization, Kalman filters, filtering based methods, and mapping data structures.
• Experienced with robot perception, localization, navigation, or SLAM through projects, research, or hands-on development.
• Familiar with computer vision fundamentals including feature extraction, feature matching, camera models, and camera calibration.
• Comfortable working with real robot sensor data from cameras, IMUs, and related onboard systems.
• Experienced with ROS or ROS 2 and familiar with tools commonly used in robotics software development.
• Able to work practically and methodically through ambiguous real-world robotics problems and turn theory into systems that run reliably on hardware.
• Curious, mathematically grounded, and excited to solve hard problems in localization, perception, and navigation.

• Experience integrating industrial robots (Fanuc, ABB, UR, MiR, Omron, etc.).
• Familiarity with PLCs, Modbus, OPC-UA, or fieldbus protocols.
• Experience with containerization (Docker, Kubernetes) and edge deployments.
• Curiosity for how machines think — and drive to make them think better.
• Passion for building the systems that make automation accessible, reliable, and fast.

Conduit is an equal opportunity employer. We welcome applications from all qualified candidates.