Robotic AI Engineer/Applied Scientist - Foundation Models
Designs Vision-Language-Action (VLA) frameworks and world models for general-purpose robots to reason, adapt, and achieve high task success in complex environments. Requires MS/PhD in CS/ML/Robotics, deep expertise in transformers, RL, or data systems, and PyTorch/JAX proficiency.
About the job
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).
Skills
PyTorch, JAX, Python, Transformers, Reinforcement Learning, Vla Models, Multi-Modal Alignment, Distributed Training, Model Compression, Nvidia Jetson, Robotics Kinematics, Robotics Dynamics
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