Role Summary:
As the Robotics Head, you will be responsible for driving the strategy, design, and execution of advanced robotic systems, including quadrupeds, bipeds, humanoids, bionic robots, and specialized industrial robotics solutions. This role requires deep expertise in robotics, AI, and engineering, combined with strong leadership and strategic planning abilities.
Key Responsibilities:
1. Strategic Leadership and Vision
• Comprehensive Robotics Strategy across Diverse Platforms: Develop a multi-faceted robotics strategy that encompasses quadrupeds, bipeds, humanoid, and bionic robots, as well as industrial robotic systems. Balance near-term goals with a forward-looking vision that keeps the company at the forefront of robotics innovation.
• Cross-Functional System Integration: Lead the Perception, Mechanical, and Edge AI teams in achieving seamless integration across robotics subsystems. Ensure mechanical, sensory, and computational components work together to create cohesive, reliable platforms adaptable to industrial use cases.
• Technology Roadmap: Establish and oversee a robotics roadmap that includes advancements in robotic locomotion, perception, and AI. Guide the development of each robotic platform from early design through deployment, ensuring alignment with organizational objectives and market demands.
2. Oversee Development of Quadruped, Biped, Humanoid, and Bionic Robots
• Quadruped and Biped Robotics: Guide the design and development of quadruped and biped robotic platforms optimized for agility, stability, and robustness in dynamic environments. Emphasize advanced control algorithms for balance, navigation, and adaptability in unpredictable industrial settings.
• Humanoid Robotics: Direct the design and functionality of humanoid robots capable of performing complex, human-like tasks. Oversee developments in kinematic and dynamic modeling to ensure accurate motion control, especially for applications requiring high dexterity and precise manipulation.
• Bionic Robotics: Lead R&D efforts in bionic robotics, focusing on integrating bio-inspired designs that enhance physical performance and adaptability. Collaborate with biomechanics experts to develop systems that mirror biological movements and utilize advanced materials for flexible, resilient structures.
• Industrial Robotic Systems: Oversee the development of industrial-grade robots tailored for specific tasks in manufacturing and automation. Ensure systems are built for durability, safety, and precision in high-demand environments, integrating real-time AI for efficient operation.
3. Lead Perception Team for Advanced Sensing and Vision
• Develop High-Performance Perception Systems: Guide the Perception team in creating sophisticated sensing systems that provide comprehensive environmental understanding. Develop and deploy sensor fusion techniques that integrate data from LiDAR, depth cameras, and stereo vision for accurate 3D mapping.
• Real-Time Computer Vision and Object Detection: Oversee the application of deep learning models for real-time computer vision, focusing on tasks such as object detection, classification, and scene segmentation. Enable dynamic obstacle avoidance and precision in navigation.
• SLAM and Localization: Drive the implementation of SLAM (Simultaneous Localization and Mapping) algorithms to allow autonomous navigation and localization within complex environments. Ensure SLAM solutions are optimized for deployment across humanoid, bionic, quadruped, and industrial robotics systems.
4. Direct Mechanical Engineering and Robotics Design
• Mechanical Architecture for Advanced Robots: Lead the Mechanical Engineering team in developing highly resilient and scalable robotic platforms, focusing on efficient power transmission, lightweight materials, and ergonomic design for biped, quadruped, and humanoid robots.
• Precision Motion Control and Actuation: Oversee the design of motion control and actuation systems that meet the specific needs of humanoid and bionic robots, ensuring seamless and lifelike movement. Emphasize balance control, load distribution, and adaptive motion planning.
• Prototyping, Validation, and Iteration: Establish rigorous prototyping and testing processes for new robotic designs. Ensure iterative validation cycles to assess performance in both simulated and real-world environments, aligning with industry standards for durability and safety.
5. Lead Edge AI Team for Real-Time Processing
• Edge AI System Optimization: Direct the Edge AI team in optimizing real-time processing capabilities on robotic platforms, particularly for tasks involving perception and autonomous decision-making. Leverage edge devices (e.g., NVIDIA Jetson) to minimize latency and dependency on cloud infrastructure.
• AI Model Deployment on Edge Platforms: Ensure that perception and control models are efficiently deployed on edge hardware with tools like TensorRT and CUDA. Optimize resource utilization and performance to meet the computational demands of humanoid, quadruped, and industrial robots.
• Continuous Learning and MLOps: Implement MLOps best practices for model monitoring, feedback, and retraining. Enable robots to learn and adapt through continuous model improvements, enhancing their autonomy and responsiveness to environmental changes.
6. High-Level Project and Resource Management
• Define and Monitor Key Milestones and Objectives: Establish key milestones and objectives for each robotics platform under development. Regularly assess progress toward deliverables and adjust timelines as necessary to achieve strategic and operational goals.
• Resource and Budget Allocation: Strategically allocate resources and manage budgets across Perception, Mechanical, and Edge AI teams. Work closely with finance and executive teams to secure funding and ensure efficient use of resources for maximum project impact.
• Risk Management and Contingency Planning: Identify potential risks and implement mitigation strategies to address challenges in robotics development and integration. Establish protocols for handling issues that arise during testing, deployment, or scaling.
7. Executive Team Development and Mentorship
• Cross-Functional Team Leadership: Foster a collaborative and high-performing team environment across Perception, Mechanical, and Edge AI departments. Hold regular cross-functional meetings to promote knowledge sharing, alignment, and problem-solving.
• Mentorship and Talent Development: Provide mentorship and career development opportunities for team members. Conduct performance evaluations, set development goals, and support continuous learning and skill advancement.
• Recruitment and Retention: Collaborate with HR to attract top talent in robotics, AI, and engineering fields. Develop retention strategies to build and maintain a team capable of achieving the company's vision for advanced robotics.
8. Innovation, R&D, and Industry Engagement
• Drive Robotics Innovation and R&D: Lead R&D initiatives into cutting-edge robotics technologies, focusing on emerging methodologies in control, adaptability, and bio-inspired robotics. Prioritize exploratory projects that enhance robotic capability and adaptability.
• Engage in Industry Partnerships and Open-Source: Build relationships with research institutions, industry leaders, and the open-source community. Encourage the team's involvement in industry events, open-source projects, and collaborative R&D to stay at the forefront of robotics innovation.
• Develop Intellectual Property (IP): Work closely with legal teams to establish a strategy for protecting intellectual property. Encourage patent filings for novel designs, methodologies, and systems developed within the robotics teams.
9. Compliance, Safety, and Performance Standards
• Optimize Robotics for Industrial-Grade Performance: Ensure that robotic systems meet high standards for performance, reliability, and safety. Oversee efforts to meet real-time requirements, enhance durability, and optimize efficiency for industrial environments.
• Establish Safety and Compliance Protocols: Implement rigorous safety standards across all robotics projects, ensuring compliance with industry regulations. Conduct risk assessments and validate that systems adhere to both internal and regulatory safety guidelines.
• Ethics and Accountability in AI: Promote ethical practices in AI and robotics development, ensuring transparency and accountability in all autonomous decision-making processes. Maintain adherence to best practices in ethical AI and robotics.
Required Qualifications:
• Education: Master's or Ph.D. in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, or a related field.
• Experience: • 15+ years of experience in robotics, with specific expertise in humanoid, biped, quadruped, bionic, and industrial robotic systems. • 7+ years in a senior leadership role, managing multidisciplinary robotics and engineering teams. • Proven track record of designing and deploying autonomous robotic systems for industrial and manufacturing environments.
• Technical Skills: • Advanced Robotics and Perception: Expertise in LiDAR, 3D mapping, SLAM, sensor fusion, and computer vision for robotic perception systems. • Mechanical Engineering and Motion Control: Extensive knowledge of robotic kinematics, actuation, and control for complex robotic systems, including humanoids and quadrupeds. • Edge AI and Real-Time Processing: Skilled in deploying AI models on edge devices with tools like TensorRT and CUDA, and optimizing systems for low-latency, high-performance applications. • Software Development: Proficiency in Python and C++ for ROS integration, simulation, and robotic control. • Robotics Frameworks: Extensive experience with ROS (Robot Operating System) and ROS2 for developing and integrating robotic systems. • AI and Machine Learning: Proficiency in TensorFlow, PyTorch, and scikit-learn for developing and implementing machine learning models in robotics applications. • Computer Vision Libraries: Strong knowledge of OpenCV, PCL (Point Cloud Library), and other relevant computer vision libraries for robotic perception. • Simulation Tools: Experience with Gazebo, Webots, or similar robotics simulation environments for testing and development. • Control Systems: Familiarity with control theory and implementation using libraries like control_toolbox or custom solutions. • Embedded Systems: Knowledge of programming for embedded systems, including real-time operating systems (RTOS) and microcontroller programming. • Version Control and Collaboration: Proficiency with Git and collaborative development platforms like GitHub or GitLab. • CI/CD for Robotics: Experience with continuous integration and deployment tools adapted for robotics development, such as Jenkins or GitLab CI. • Data Analysis and Visualization: Skill in using tools like Matplotlib, Plotly, or RViz for data analysis and visualization in robotics contexts. • Hardware Interfaces: Familiarity with communication protocols like I2C, SPI, CAN, and experience interfacing with various sensors and actuators.
• Cloud Robotics: Experience with cloud platforms like AWS RoboMaker or Google Cloud Robotics for distributed computing and cloud-based robotics applications.
• Reinforcement Learning: Proficiency in implementing RL algorithms for robotics, using frameworks like OpenAI Gym or RLlib.
• CAD and 3D Modeling: Skill in using CAD software like SolidWorks or Fusion 360 for designing robotic components and systems.
• Parallel Computing: Experience with CUDA or OpenCL for GPU acceleration in robotics applications.
• Robotic Middleware: Familiarity with middleware solutions like YARP or OROCOS for real-time control and communication in complex robotic systems.
Preferred Qualifications:
• Project and Resource Management: Experience with Agile methodologies, project management tools, and budget management for complex robotics projects. Demonstrated ability to manage multiple cross-functional teams effectively and ensure timely project completion.
• MLOps and DevOps: Familiarity with MLOps practices for model deployment, monitoring, and retraining in production environments, as well as DevOps practices for CI/CD pipelines in robotics.
• Manufacturing and Industrial Compliance: Knowledge of industry safety standards, regulatory compliance, and the unique challenges associated with deploying robotics in manufacturing and industrial settings.
• Open-Source Contribution and Community Engagement: Proven experience contributing to open-source robotics, AI, or automation projects, with a commitment to leveraging and advancing community-driven innovation.