{"id":4018520,"url":"https://alion.io/job/constructor-labs-motion-planning-control-engineer-2","title":"Motion Planning & Control Engineer","company":{"id":720007,"name":"Constructor Labs","domain":"constructorlabs.org","url":"https://alion.io/company/constructorlabs","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-10T06:52:35Z"}},"role":"Robotics","role_family":"Robotics","seniority":null,"employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Luxembourg City, Luxembourg"],"countries":["LU"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":true,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Linux","optional":false},{"name":"Model Predictive Control","optional":false},{"name":"Motion Planning","optional":false},{"name":"Obstacle Avoidance","optional":false},{"name":"Path Planning","optional":false},{"name":"Python","optional":false},{"name":"ROS","optional":false},{"name":"MariaDB","optional":true}],"status":"live","first_seen_at":"2026-11-10T13:17:21Z","employer_posted_date":"2026-11-10","last_verified_at":"2026-11-10T14:04:48Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About us \nConstructor Labs is the research arm of Constructor Knowledge, based at Campus Ring 1 in Bremen, Germany. Our work is organized around three units: Knowledge Labs, Cyber Labs and Science Labs. We apply computing, software and robotics research to make scientists, teachers and students more effective, working with institutional partners that include CERN, NUS, Carnegie Mellon and Fraunhofer.\nOur EdTech practice produces research that runs in production. AI-supported remote proctoring is in use at the Irish Aviation Authority, Princess Nourah University and Saudi Electronic University, and our course delivery platform is used by CERN, EPFL, MariaDB and Acronis. For industry clients, we build tailored BSc, MSc and PhD pipelines, L&D operating models, AI upskilling programmes and customer training systems.\nConstructor Autonomous Racing builds the driving software for a full-size autonomous race car. We race in the A2RL (Abu Dhabi Autonomous Racing League). We also work with automotive partners to transfer our technology to road cars. Our R&D projects include emergency obstacle avoidance, ADAS and an AI driving coach (Racing Coach).\nAbout the Role\nWe're looking for a Motion Planning & Control Engineer to join our autonomous racing team and help develop the algorithms that enable a full-size race car to navigate the track autonomously at high speeds.\nIn this role, you'll work at the intersection of control theory, robotics, and vehicle dynamics, developing and optimizing motion planning and control algorithms for real-world racing conditions. You'll be involved throughout the development cycle, from mathematical modelling and simulation to implementation, testing, and validation on the actual race car.\nYou'll work closely with the engineering team to improve vehicle performance, stability, and reliability, with opportunities to see your solutions tested during track sessions and international racing events.\nBeyond autonomous racing, you'll also contribute to transferring our technology into real-world automotive applications, including advanced driver assistance systems and emergency obstacle avoidance.\nWhat you'll do\nDevelop and optimize advanced motion control algorithms, focusing on nonlinear Model Predictive Control (MPC), using C++ and ROS 2 to ensure reliable, real-time performance on our autonomous race car.\nDesign, develop, and validate vehicle dynamics models, including system identification, to support accurate motion control, trajectory planning, and simulation.\nContribute to the development and improvement of motion planning algorithms, including MPC-based path planning, Model Predictive Path Integral (MPPI) control, and zone-based planning approaches.\nValidate and refine algorithms through simulation and real-world testing, ensuring a smooth transition from virtual environments to on-track deployment.\nParticipate in track testing sessions and international racing events, supporting vehicle operations, troubleshooting technical challenges, and analyzing performance data to continuously improve our autonomous driving systems.\nCollaborate on cutting-edge automotive R&D projects, adapting and transferring autonomous racing technologies to real-world automotive applications, including advanced driver assistance systems (ADAS) and emergency obstacle avoidance.\nWhat we're looking for\nAn MSc or PhD in Robotics, Control Engineering, Autonomous Systems, Automotive Engineering, or a closely related field.\nHands-on experience with Model Predictive Control (MPC), ideally through a master's thesis, PhD research, or autonomous racing project.\nPractical programming experience with C++, Python, ROS/ROS 2, and Linux.\nExperience developing motion planning or control algorithms for autonomous vehicles or mobile robotic systems.\nA solid understanding of control theory, vehicle dynamics, and motion planning principles.\nExperience implementing and testing algorithms in simulation and, ideally, on real robotic platforms or vehicles.\nStrong analytical and problem-solving skills, with an interest in applying research to real-world autonomous racing challenges.\nFluent English and the ability to collaborate in an international engineering team.\nNice to have\nParticipation in Formula Student Driverless, A2RL, Indy Autonomous Challenge, F1TENTH, or similar autonomous racing competitions.\nResearch experience focused on nonlinear MPC, trajectory optimization, or sampling-based motion planning.\nFamiliarity with vehicle dynamics modelling, tyre modelling, and system identification.\nExperience with drive-by-wire systems and CAN communication protocols.\nWhat we offer\nThe opportunity to work on cutting-edge autonomous racing technology, contributing directly to the development and performance of a full-size autonomous race car.\nHands-on involvement in real-world testing and international racing events, with opportunities to travel to locations such as Abu Dhabi and Shanghai.\nA Luxembourg-based position with a hybrid working arrangement, combining engineering development with practical testing activities.\nVacation days 25\nRelocation package if you live outside of Luxembourg\nLanguage classes\nNewborn bonus","description_format":"text","description_chars":5179,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"Advanced 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