Trener Robotics develops Acteris, a physical-AI platform that lets manufacturers program existing industrial robots through natural language and self-learning skills.
Location: On-site in San Jose, California, USA or Trondheim, Norway Reports to: VP of Research & Development Seniority: Senior – Staff Level Help make physical AI a reality. At Trener Robotics, we are building the future of industrial automation through intelligent agents that understand natural language and control robotic stations. Our product, Acteris, bridges AI and robotics to deliver flexible, user-friendly automation. We are a fast-growing, venture-backed company entering a phase of rapid scale. Our mission is to make AI-powered robotics accessible, and we are building the team that will make it real. Now, we are looking for a Robotics Motion Planning and Control Engineer to own the reliability, safety, and performance of robot manipulation across our entire product line. YOUR MISSION Own the motion planning and real-time control stack that makes our robots behave safely and predictably in the real world — across multiple robot brands, environments, and tasks. You will be responsible for the full pipeline: from trajectory generation and online replanning to sensor-guided execution and hardware interfacing. This role demands deep technical ownership, a research-informed mindset, and the engineering rigor to ship production-grade systems into live industrial deployments. You will work at the intersection of classical control theory, modern optimization, and learned robot behavior — collaborating with perception, AI, and systems engineers to push the frontier of what our platform can reliably do. KEY RESPONSIBILITIES - Own the motion planning and control architecture for robot manipulators across multiple hardware platforms, ensuring consistent, safe, and reliable execution in unstructured industrial environments. - Design and implement real-time control pipelines capable of reacting to dynamic environments, sensor feedback, and task-level constraints — with a focus on robustness and predictable failure behavior. - Develop trajectory optimization and replanning capabilities that handle kinematic and dynamic constraints, joint limits, obstacle avoidance, and task-space objectives simultaneously. - Integrate perception and sensor feedback into closed-loop control, enabling robots to correct, adapt, and recover during execution based on visual and force/torque signals. - Collaborate with AI and behavior teams to ground learned policies and high-level task plans into physically sound, executable motion — bridging the gap between AI output and real-world constraints. - Define safety and constraint frameworks that encode workspace limits, self-collision avoidance, human proximity rules, and operational boundaries in a principled, verifiable way. - Develop simulation environments and hardware-in-the-loop test rigs to validate planning and control algorithms before and during live deployment. WHAT WE ARE LOOKING FOR - Proven experience designing and deploying motion planning and real-time control systems for robot manipulators. - Deep understanding of both sampling-based and optimization-based planning approaches, and the practical judgment to know when each is appropriate. - Strong background in model-based and predictive control methods, including experience applying them to physical robotic systems under real-time constraints. - Hands-on experience integrating perception data — including vision — into reactive control loops, and a principled understanding of the failure modes that arise when doing so. - Familiarity with constraint specification and enforcement at the control level, including the ability to encode safety, task, and operational requirements in a structured, maintainable way. - Experience writing production-quality C++ and Python for robotics, with rigorous testing and reproducible builds. - Comfort working across hardware boundaries: robot controllers, communication protocols, sensors, grippers, and the full signal chain from planning to actuation. - Strong mathematical foundations in optimization, control theory, and geometry as they apply to robot motion. Particularly strong candidates will have: - Research or applied experience with learned motion generation, including exposure to modern data-driven approaches to robot behavior and their integration with classical planning and control. - Familiarity with formal or certificate-based approaches to guaranteeing safety properties in control systems. - Experience working across multiple robot brands and navigating the integration challenges that come with vendor-specific APIs and controller behaviors. - A publication record or open-source contributions in motion planning, control, or manipulation. WHAT YOU WILL GET - Full ownership of the motion planning and control layer of a category-defining automation platform — with direct impact on product reliability and customer outcomes. - The opportunity to work hands-on with real robots from day one. - A seat at the table where research meets product: your technical decisions will shape the architecture of the platform for years to come. - Collaboration with a high-caliber team across AI, robotics, systems engineering, and product — all aligned on a clear and ambitious mission. - A fast-moving, mission-driven environment where rigorous engineering and research-informed thinking are equally valued.