Responsibilities
- Design and implement data-driven control architectures that process complex, multi-modal sensor inputs to enable real-time decision-making in dynamic physical environments.
- Lead the development of high-fidelity, physics-based simulation environments to validate system performance and preempt real-world operational failures.
- Integrate large-scale real-world sensor data with targeted synthetic datasets to create unified training pipelines for foundational AI models.
- Apply reinforcement learning, imitation learning, and behavior cloning methods to develop adaptable robotic capabilities across varied industrial tasks.
- Optimize machine learning models for low-latency, high-reliability deployment on edge devices across a global network of manufacturing facilities.
Benefits
- Competitive salary & equity
- Unlimited paid time off
- Comprehensive medical, dental, vision, and retirement benefits
Compensation
Competitive salary & equity
Other
Daily meals provided with your own choice
