Robotics Research Engineer: Foundation Models

General Robotics; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

General Robotics is hiring Research Engineers to develop foundation models for GRID, spanning architecture, large-scale training, reinforcement learning, simulation, and evaluation to enable robotic behaviors that generalize across tasks, environments, and embodiments.

The Singapore team collaborates with the US team on GRID R&D and supports regional customers; applicants should have strong ML/robotics background and a track record of publishing or impactful research.

Qualifications

• Bachelor's degree or equivalent in CS/Robotics or related field.
• Research or applied ML/robotics/CV experience.
• Strong Python and PyTorch skills.
• Experience developing ML models or robotics algorithms.
• Experience with at least one robotics simulation platform.

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, RL, and evaluation.
• Scale existing simulation platforms for data generation and RL across heterogeneous scenes and tasks.
• Advance generalization beyond demonstrated tasks and robust spatial understanding.
• Improve sim-to-real transfer and reliability under real-world constraints.
• Develop Auto-Engineering methods for scalable deployment.
• Communicate research as reusable, production-quality work.

Skills

Python programming

PyTorch

machine learning

robotics

computer vision

research ability

communication

Education

Bachelor's degree in Computer Science, Robotics, a related technical field, or equivalent practical experience

MS or PhD in Machine Learning, Robotics, Computer Science, or a related field, or equivalent industry research experience

Tools

NVIDIA Isaac Sim

MuJoCo

ManiSkill

Job description

General Robotics is hiring Research Engineers to develop foundation models for GRID, spanning architecture, large-scale training, reinforcement learning, simulation, and evaluation to enable robotic behaviors that generalize across tasks, environments, and embodiments.

The Singapore team collaborates with the US team on GRID R&D and supports regional customers; applicants should have strong ML/robotics background and a track record of publishing or impactful research.