About this Role
We are looking for strong candidates who have a background in robotics and machine learning, with experience in areas like multimodal machine learning; computer vision; LLMs/VLMs/VLAs; diffusion/flow matching; reinforcement learning and imitation learning for robotics; efficient training and inference.
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.
Platform Development & Data Collection: Translate field-tested patterns and customer requirements into scalable, automated features for the GRID platform, and lead data collection campaigns as well as model training for specific customer projects (including synthetic data from simulations, teleoperations, and motion capture).
Minimum Qualifications
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.
Desired Qualifications
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.
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.
Base Salary Range
The typical base pay range for this role is 82000 SGD - 220000 SGD. Your actual pay will be based on factors such as skills and experience.
Equal Opportunity Employer
General Robotics is an equal opportunity employer. We do not discriminate on the
basis of any status protected by applicable law.
Accommodations
If you need a reasonable accommodation during the application or interview
process, please contact: [email protected]
