generalrobotics - Robotics Engineer - Singapore: Foundation Model Training
Requirements
• Bachelor's degree in Computer Science, Robotics, a related technical field, or equivalent practical experience. • Research or applied experience in machine learning, robotics, or computer vision. • Strong Python programming skills and experience with PyTorch. • Experience developing and evaluating machine-learning models or robotics algorithms. • Experience with at least one robotics simulation platform. • Familiarity with inverse kinematics, dynamics, and robotic manipulation. • Demonstrated research ability through publications, substantial research contributions, or equivalent industry work. • Must obtain and maintain work authorization in the country of employment. • MS or PhD in Machine Learning, Robotics, Computer Science, or a related field, or equivalent industry research experience. • First-author publications at NeurIPS, ICML, ICLR, CVPR, CoRL, RSS, or ICRA. • Hands-on experience with Vision-Language-Action models, world models, world action models, diffusion or flow-matching policies, transformers, reinforcement learning, or imitation learning. • Experience with large-scale pretraining, data-mixture design, post-training, or reinforcement learning. • Experience scaling simulation platforms such as NVIDIA Isaac Sim or Isaac Lab, MuJoCo, or ManiSkill for learning and data generation. • Experience with sim-to-real transfer and evaluation on real robotic systems. • A track record of taking research from hypothesis through implementation and robust validation.
Responsibilities
• Formulate research hypotheses and design rigorous experiments informed by deployment needs. • Develop and scale foundation models for robotic perception and action. • Leverage simulation and diverse datasets for pretraining, post-training, reinforcement learning, and evaluation. • Scale existing simulation platforms for data generation and reinforcement learning across heterogeneous scenes and tasks. • Advance generalization beyond demonstrated tasks and robust spatial understanding. • Improve sim-to-real transfer and reliability under real-world deployment constraints. • Develop Auto-Engineering methods for scalable and robust deployment. • Implement, evaluate, and clearly communicate research as reusable, production-quality work.
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