Robotics Engineer - Singapore: Foundation Model Training

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

About this Role

We're hiring Research Engineers to develop foundation models for GRID. You'll work across model architecture, large-scale training, reinforcement learning, simulation, and evaluation to build robotic behaviors that generalize across tasks, environments, and embodiments.

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.

Minimum Qualifications

• 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.

Desired Qualifications

• 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.

About General Robotics

General Robotics is building the intelligence grid for physical AI — the platform that makes any robot, from robotic arms to humanoids, genuinely intelligent. Headquartered in Redmond, Washington, we're venture backed, including by Accenture, who invested in General Robotics in 2026 to advance Physical AI‑powered robotics in manufacturing and logistics, and we're also part of Microsoft's Startups Pegasus Program. Our team's work spans some of the most widely adopted robotics and AI research to come out of Microsoft Research, Google Research and DeepMind — including AirSim, PACT, ClimaX, Tensorflow Object Detection and VideoPoet.

Our Singapore team works closely with the US team on GRID's research and development as an extended research facility, and to support our customers in the region. Engineers from both offices regularly visit each other for collaboration.