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Scaled Foundations (operating as General Robotics) is hiring Research Engineers to develop foundation models for GRID, an intelligence platform for physical AI that powers robots from robotic arms to humanoids. 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. The company is venture-backed, including by Accenture (2026 investment), and part of Microsoft's Startups Pegasus Program. The team draws from widely adopted robotics and AI research from Microsoft Research, Google Research, and DeepMind.
Key Responsibilities:
- Formulate research hypotheses and design rigorous experiments informed by real 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 RL across heterogeneous scenes and tasks
- Advance generalization beyond demonstrated tasks and improve 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 communicate research as reusable, production-quality work
The company values exceptional depth in one or more relevant areas; candidates are not expected to have experience across all domains. General Robotics is headquartered in Redmond with an extended research facility in Singapore; engineers from both offices regularly collaborate.
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, Isaac Lab, MuJoCo, or ManiSkill for learning and data generation
- Experience with sim-to-real transfer and evaluation on real robotic systems
- Track record of taking research from hypothesis through implementation and robust validation
