Job Description
Nexus Dynamics is pioneering the Vision 2026 initiative—a revolutionary leap in autonomous perception and spatial computing. We are seeking a visionary Lead Robotics Engineer to architect the next generation of AI-driven perception systems. If you are passionate about pushing the boundaries of what is possible in robotics and are ready to shape the future of mobility, this is your opportunity to lead a world-class engineering team.
In this role, you will bridge the gap between cutting-edge deep learning and real-world hardware constraints. You will work in a fast-paced, high-impact environment where your code will directly influence the autonomy of our fleet of robots.
Responsibilities
- Architect Perception Systems: Design and implement robust computer vision and sensor fusion algorithms capable of operating in complex, unstructured environments.
- Optimize Performance: Develop and optimize deep learning models for edge deployment, ensuring low latency and high throughput on embedded hardware.
- Research & Development: Lead internal research initiatives, exploring novel architectures and methodologies to stay ahead of industry standards for the year 2026.
- Mentorship: Guide a team of talented software engineers and data scientists, fostering a culture of innovation and technical excellence.
- System Integration: Collaborate closely with the hardware engineering team to ensure seamless integration of software components with physical sensors and actuators.
- Prototyping: Rapidly prototype and validate new features using simulation tools and real-world testbeds.
Qualifications
- Education: Master’s or PhD degree in Robotics, Computer Science, Electrical Engineering, or a related field.
- Experience: 5+ years of experience in robotics, computer vision, or autonomous systems, with at least 2 years in a leadership or senior engineering role.
- Programming: Expert proficiency in Python and C++, with deep understanding of software design patterns and systems engineering.
- Frameworks: Extensive experience with deep learning frameworks (PyTorch, TensorFlow) and computer vision libraries (OpenCV, ROS/ROS2).
- Hardware: Hands-on experience deploying models on embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) or edge GPUs.
- Problem Solving: Demonstrated ability to troubleshoot complex system-level issues and deliver robust solutions under tight deadlines.