AI Engineer for Robotics (The Machine Intelligence Architect)

Are you passionate about embedding artificial intelligence into robotic systems to create intelligent, adaptive, and autonomous machines? Do you have the technical skills to build algorithms that empower robots to perceive, learn, and make decisions? If you’re ready to design the AI that gives robots their "smarts," our client has the ideal role for you. We’re looking for an AI Engineer for Robotics (aka The Machine Intelligence Architect) to develop and implement AI-driven solutions that push the boundaries of robotics innovation.

As an AI Engineer for Robotics at our client, you’ll work closely with software, hardware, and robotics teams to create algorithms for perception, navigation, manipulation, and autonomous behavior. Your role will be pivotal in designing robots that can analyze environments, adapt to new tasks, and improve over time.

Key Responsibilities:

1. Design and Implement AI Models for Robotic Systems:
• Develop AI models for object detection, path planning, obstacle avoidance, and decision-making. You’ll leverage machine learning, deep learning, and reinforcement learning techniques to enhance robotic intelligence.
2. Build Perception and Computer Vision Capabilities:
• Integrate computer vision algorithms for perception, recognition, and scene understanding. You’ll enable robots to interpret visual data and interact intelligently with their surroundings.
3. Develop Algorithms for Autonomous Navigation and Path Planning:
• Design algorithms that allow robots to navigate dynamic environments autonomously. You’ll work on SLAM (Simultaneous Localization and Mapping), path planning, and motion control for safe and efficient movement.
4. Implement Reinforcement Learning for Adaptive Behavior:
• Use reinforcement learning and other adaptive algorithms to enable robots to learn from experience and improve performance. You’ll create systems that can self-optimize and respond to new tasks and challenges.
5. Optimize Models for Real-Time Performance on Embedded Systems:
• Adapt and compress AI models for deployment on embedded or edge computing platforms. You’ll ensure that models run efficiently on hardware-constrained robotic systems.
6. Collaborate with Cross-Functional Teams on System Integration:
• Work closely with robotics, control, and software engineers to integrate AI models with mechanical and electronic systems. You’ll ensure seamless interaction between AI-driven behaviors and physical components.
7. Test, Validate, and Fine-Tune AI Systems:
• Conduct rigorous testing in simulation and real-world environments to validate AI model performance. You’ll troubleshoot, analyze, and refine models to handle edge cases and ensure reliability.

Requirements

Required Skills:

• Proficiency in Machine Learning and Deep Learning: Extensive experience with frameworks such as TensorFlow, PyTorch, or Keras, particularly for applications in robotics.
• Computer Vision and Perception Expertise: Strong knowledge of computer vision techniques (e.g., object detection, segmentation) and experience with OpenCV or similar libraries.
• Reinforcement Learning and Adaptive Systems: Familiarity with reinforcement learning algorithms for training robots to adapt and improve autonomously.
• Programming Skills in Python and C++: Proficiency in Python for AI development and C++ for real-time deployment and integration with robotic systems.
• Embedded and Edge Computing Knowledge: Experience optimizing and deploying AI models on edge devices (e.g., NVIDIA Jetson, ARM) for real-time robotic applications.

Educational Requirements:

• Bachelor’s or Master’s degree in Artificial Intelligence, Robotics, Computer Science, or a related field. Equivalent experience in AI and robotics may be considered.
• Relevant certifications or coursework in machine learning, AI, or robotics are advantageous.

Experience Requirements:

• 3+ years of experience in AI or machine learning engineering, with a focus on robotics or autonomous systems.
• Experience with robotic operating systems such as ROS (Robot Operating System) and simulation environments (e.g., Gazebo) is beneficial.
• Familiarity with sensor data processing (e.g., LIDAR, RGB-D cameras) and SLAM techniques is a plus.

Benefits

• Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
• Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.
• Work-Life Balance: Flexible work schedules and telecommuting options.
• Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.
• Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.
• Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.
• Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.
• Tuition Reimbursement: Financial assistance for continuing education and professional development.
• Community Engagement: Opportunities to participate in community service and volunteer activities.
• Recognition Programs: Employee recognition programs to celebrate achievements and milestones.