Job Description
Are you ready to define the future of autonomous systems?
2026 Robotics is seeking a visionary Lead Robotics Engineer to spearhead our next-generation autonomous manufacturing solutions. We are not just building robots; we are architecting the ecosystem that will define the industrial landscape of 2026 and beyond.
In this pivotal role, you will bridge the gap between complex algorithms and physical reality. You will lead a team of elite engineers to develop cutting-edge AI-driven robotics that push the boundaries of precision, speed, and adaptability. If you are passionate about the intersection of hardware, software, and artificial intelligence, this is your opportunity to leave a lasting legacy.
Why Join Us?
- Impact: Your code will power the factories of tomorrow, directly influencing global supply chains.
- Innovation: Work with the latest in Neural Networks, LIDAR technology, and edge computing.
- Equity: Competitive stock options in a high-growth unicorn startup.
Responsibilities
- Architect Design: Design and implement robust, scalable software architectures for autonomous mobile robots and manipulators.
- Algorithm Development: Spearhead the development of advanced path planning, SLAM (Simultaneous Localization and Mapping), and object recognition algorithms.
- System Integration: Oversee the integration of hardware components (sensors, actuators, processors) with high-level software frameworks.
- Team Leadership: Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous improvement.
- R&D Leadership: Stay ahead of industry trends in AI and robotics, driving the research and development roadmap for 2026 and future iterations.
Qualifications
- Education: Master’s or Ph.D. in Robotics, Computer Science, Electrical Engineering, or a related field.
- Experience: 5+ years of professional experience in robotics software development, preferably in autonomous systems or industrial automation.
- Technical Skills: Proficiency in C++, Python, ROS (Robot Operating System), and experience with GPU acceleration (CUDA/OpenCL).
- Algorithms: Deep understanding of linear algebra, probability theory, and optimization techniques.
- Hardware Knowledge: Experience with sensor fusion (LIDAR, IMU, RGB-D) and embedded systems (Linux, RTOS).