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generalrobotics - Robotics Engineer - Singapore: AI Specialist

Singapore$64k - $64k2d ago

In Office APAC Artificial Intelligence Robotics Mechanical Engineer C++Python PyTorch Learning & Development ROAS Loom

Requirements

• Undergraduate or Master’s degree (completed or in progress) in Robotics, Computer Science, or a related technical field, or equivalent practical experience. • Current and ongoing work authorization in the country of employment. • Research experience in machine learning, robotics, and computer vision. • Experience with developing robotics algorithms or machine learning models at scale. • Programming experience in Python/C++. Good understanding of deep learning frameworks like Pytorch or Jax. • Master's/PhD in Robotics, Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. • Direct experience in robotics, computer vision, or machine learning research. • First author publications at peer-reviewed AI and robotics conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICRA, IROS, CORL). • Experience with high fidelity simulation platforms such as AirSim, CARLA, Isaac Sim, Mujoco, Maniskill among others. • Experience building efficient robotics pipelines involving sensor fusion and model inference • Experience working with modern computer vision algorithms and sensors (RGB, RGB-D cameras, LIDAR.), 3D (meshes, point clouds, etc.), segmentation, tracking, detection. • Good understanding of systems considerations and the ability to factor these into model choices.

Responsibilities

• Design methods, tools, and infrastructure to push forward the state of the art in robotics and foundation models. • Develop and adapt advanced AI models, such as Vision-Language-Action (VLA) models, to robotics applications (e.g., computer vision, SLAM, navigation). • Define research goals informed by practical engineering concerns. • Adapt standard machine learning models and frameworks to robotics applications (e.g. computer vision, SLAM, navigation). • Work with high fidelity simulation platforms for synthetic data generation. • Design data loading and training pipelines for large scale foundation model training. • Contribute to experiments, including designing experimental details, writing reusable code, running model evaluations, and organizing results. • Contribute to publications and open-sourcing efforts.