Autonomous Machines - ARC Professional

Bachelor of Engineering

Global Delivery

• Design and develop Simultaneous Localization and Mapping (SLAM) algorithms for robust localization and mapping in indoor and outdoor environments.
• Research, implement, and evaluate Vision-Language Navigation (VLN) models for instruction-guided autonomous navigation.
• Develop and deploy Vision-Language-Action (VLA) models enabling robots to perceive, reason, and execute complex manipulation tasks.
• Build autonomous navigation solutions using path planning, motion planning, obstacle avoidance, and trajectory optimization techniques.
• Work with robotic platforms including wheeled robots, UGVs, UAVs, mobile manipulators, and drone-based manipulation systems.
• Develop kinematic and dynamic models, including forward kinematics, inverse kinematics, motion control, and whole-body planning for robotic manipulators.
• Integrate and optimize robotic software using ROS/ROS 2, sensor fusion frameworks, and distributed robotic architectures.
• Create high-fidelity simulation environments using Isaac Sim, Gazebo, Omniverse, AirSim, MuJoCo, or similar simulators for development and validation.
• Implement perception pipelines leveraging computer vision, visual foundation models, multimodal AI, and sensor fusion across cameras, LiDARs, IMUs, GPS, and depth sensors.
• Utilize the NVIDIA Physical AI ecosystem, including Isaac Lab, Isaac Sim, Isaac ROS, Cosmos, Jetson, and Omniverse for robot learning and deployment.
• Develop AI-driven robotic solutions using Python, deep learning frameworks (PyTorch/TensorFlow), and reinforcement learning techniques.
• Evaluate robotic systems using simulation and real-world benchmarks, focusing on robustness, safety, scalability, and deployment readiness.

• Essential Qualification: Master's in Robotics, Computer Science, AI, Machine Learning, Electrical Engineering, or related field.
• Desired Qualification: PhD in robotics field
• Publications in Tier-1 conferences and Q1 journals, patents, or significant project contributions in robotics, autonomous systems, embodied AI, or computer vision.
• Experience deploying autonomous systems on real-world robotic platforms.

• Embodied AI and Autonomous Agents, Semantic Mapping and Scene Understanding, 3D Computer Vision and Point Cloud Processing, Grasp Planning and Manipulation, Multi-Robot Coordination and Swarm Robotics, Model Predictive Control (MPC), Autonomous Exploration and Frontier Planning, Navigation using Vision Foundation Models (CLIP, DINOv2, SigLIP), Digital Twins and Sim-to-Real Transfer, Safety-Critical Autonomous Systems, Edge AI deployment on NVIDIA Jetson platforms, Warehouse Automation, Industrial Robotics, and Field Robotics

• Technology->AI-Data science->Computer Vision