Robotic AI Engineer/Applied Scientist - Foundation Models

Responsibilities

• Architect Embodied Foundations: Design model architectures (VLA, World Models, etc.) that achieve ultra-high task success rates with minimal human demonstrations.
• Master Data Efficiency: Develop novel co-training strategies and efficient learning algorithms that leverage diverse data sources—from Internet-scale video to sparse, high-fidelity human interventions.
• Generalize Cross Embodiments: Build models capable of zero-shot or few-shot adaptation to new robot configurations, maintaining a high success rate even when proprietary hardware and actuation systems evolve.
• Innovate Real-World RL: Formulate and deploy novel Reinforcement Learning and policy extraction methods specifically designed for physical, real-world manipulation.
• Design the Data Loop: Collaborate on advanced data collection systems to capture critical human intervention data for model bootstrapping.

Qualifications

Must-have:

• MS or PhD in CS, Robotics, Machine Learning, or a related field (or equivalent industry experience).
• Deep Technical Mastery: Advanced understanding of transformers, multi-modal alignment, and mapping perception to high-frequency motor control.
• Specialized Excellence: Proven ability to innovate within one or more areas: VLA models, Real-world RL, or large-scale Data Infrastructure.
• Software Excellence: Expert-level Python and deep familiarity with PyTorch or JAX.

Nice-to-have:

• A track record of high-impact publications (NeurIPS, ICRA, RSS, CVPR) or significant open-source contributions.
• Experience with large-scale distributed training and model compression.
• Experience with deployment of models to edge devices (NVIDIA Jetson/Orin) for real-time inference.
• General knowledge of robotics principles (kinematics, dynamics).