Lead Perception Engineer

Key Responsibilities

• Lead Technical Execution: Guide a team of engineers through the full development lifecycle, from initial concept, hardware selection and algorithm design to field deployment and maintenance.
• Select the optimal sensors (e.g., LIDARs, time-of-flight cameras, stereo vision) for various applications.
• Create AI and ML models to enhance object detection, classification, and tracking capabilities.
• Utilize deep learning techniques to improve system accuracy and efficiency.
• Conduct extensive testing, validation, and calibration to ensure system accuracy and reliability.
• Design and maintain high-throughput perception pipelines, ensuring low latency and high reliability in real-time environments.
• Active Debugging: Take a "hands-on" approach to identifying and resolving bottlenecks or failures in the perception stack, including hardware-software integration issues.
• Work with cross-functional teams, including mechanical, system, mechatronic, software and software quality assurance engineers, and domain experts to ensure solutions are aligned and integrated with product goals.

Requirements

• 8-10 years of professional software engineering experience in robotics, computer vision, or a closely related field.
• At least 2-3 years of experience in a formal or informal leadership capacity, such as a Team Lead, Tech Lead, or Mentor, with a track record of guiding technical projects and junior engineers.
• Strong foundation in robotics fundamentals, including coordinate transforms, sensor calibration, perception pipeline design, and system architecture.
• Proven ability to perform hands-on, low-level debugging and systematic troubleshooting of complex robotic systems in real-world environments.
• Expert-level proficiency in Python, C++ and/or Rust with a focus on writing production-grade, well-tested code for autonomous systems.
• Demonstrated experience designing, implementing, testing, and optimizing vision solutions for robotic or automation applications.
• Experience in building ML pipelines and optimizing/productizing ML models.
• Proficiency in AI/ML frameworks (e.g. PyTorch) and computer vision libraries (e.g., OpenCV, PCL, Open3D, CUDA).
• Familiarity with various depth sensing modalities (e.g., LiDAR, stereo vision, time-of-flight) and sensor fusion.
• Excellent problem-solving skills, attention to detail, and ability to work in a dynamic environment.