School of Engineering - Project Staff (Robotics and Unmanned Systems)

Job description

[What the role is] We are seeking a versatile and forward-thinking Development Engineer/Specialist to join our team in advancing the boundaries of Physical and Embodied AI. In this role, you will bridge the gap between intelligent software and the physical world, transforming AI models into tangible, autonomous capabilities. You will design, prototype, and deploy AI-enabled physical systems—such as robotics, unmanned platforms, and smart automation solutions—utilizing ROS2.0. You will also act as a bridge between technology, industry partners, and academia, fostering ecosystem growth through knowledge sharing, mentoring student projects, and leading live innovation showcases. If you are passionate about applied research, hands-on hardware-software integration, and inspiring others, we want to hear from you. [What you will be working on] 1. Physical & Embodied AI Development • System Design & Prototyping: Design, develop, and prototype intelligent physical systems, robotics, and advanced automation platforms leveraging ROS2.0. • Hardware-Software Integration: Integration of advanced AI models, computer vision, and operational systems with sensors, actuators, edge devices, and embedded systems. • Applied Research & Piloting: Drive feasibility studies and pilot programs involving AI-enabled automation, unmanned systems (UAVs/AGVs), and smart monitoring solutions. • End-to-End Project Support: Manage the full lifecycle of applied research and proof-of-concept initiatives, including data preparation, rigorous experimentation, performance evaluation, comprehensive documentation, and post-deployment technical support. • Prototype Commercialization: Assist in translating high-potential AI prototypes into scalable, deployable, or licensable industry solutions. 2. Knowledge Sharing & Ecosystem Enablement • Educational Mentorship: Support curriculum development and actively mentor student capstone and research projects in robotics and applied AI domains. • Stakeholder Collaboration: Partner closely with internal researchers and external industry stakeholders on collaborative innovation initiatives. • Technology Advocacy: Lead and support engaging AI and robotics demonstrations for lab visits, innovation showcases, and executive partner engagements. • Use Case Curation: Synthesize and curate compelling physical AI use cases to engage, educate, and inspire both academic and industrial audiences. [What we are looking for] • Qualification in Robotics Engineering, Computer Science, Electrical/Electronic Engineering, Mechatronics, or a closely related quantitative field. • Robotics Architecture: Proven hands-on experience developing with ROS2 (Robot Operating System). Familiarity with robotic arms and humanoid robots, robot kinematics, dynamics, and simulation tools (e.g., Gazebo, Isaac Sim) is highly valued. • AI & Computer Vision Integration: Practical experience deploying Machine Learning/Deep Learning models (e.g., PyTorch, TensorFlow) and computer vision frameworks (e.g., OpenCV) onto physical platforms. • Embedded Software Engineering: Proficiency in C++ and Python, with strong software development best practices (Git, clean architecture, code reviews). • Hardware & Edge Systems: Experience working with microcontrollers and single-board computers (e.g., NVIDIA Jetson, Raspberry Pi). Solid understanding of hardware communication protocols (I2C, SPI, UART, CAN bus) and sensor fusion (LiDAR, IMUs, cameras). Soft Skills & Core Attributes • Collaborative Mindset: Exceptional interpersonal skills with a proven ability to collaborate effectively across diverse teams of engineers, researchers, students, and corporate partners. • Communication & Presentation: Confident and articulate presenter capable of breaking down complex technical concepts into accessible demonstrations for non-technical audiences. • Passion in Educational Mentorship: Guiding student learning journeys and contributing to educational frameworks. • Adaptability: A thriving curiosity for ambiguous, fast-paced R&D environments where troubleshooting physical hardware requires creative problem-solving.