1.5 Robotics AI Engineer – Field Foundation Models & Dynamics Foundation Models
FieldAI is transforming how robots interact with the real world by building advanced AI systems for robotics. The Robotics AI Engineer will design and implement AI-driven planning and control architectures to enhance robotic performance in complex environments.
Responsibilities
- Design and implement next-generation AI-powered Field Foundation Models (FFMs) and Dynamics Foundation Models (DFMs)
- Pioneer risk-aware, uncertainty-driven decision-making architectures that enable autonomous robots to navigate unstructured, high-stakes environments
- Reimagine planning algorithms, trajectory optimization, and task scheduling as a fusion of probabilistic reasoning, geometric intelligence, and adaptive AI, ensuring precise and reliable robot motion in dynamic environments
- Work extensively with ROS (Robot Operating System) to develop, test, and integrate next-gen autonomous intelligence modules
- Design, implement, and test real-world and simulation-learning loops, where self-evolving models refine planning, control, and perception through real-world interaction
- Conduct field tests to validate performance and optimize system behavior
- Collaborate with hardware teams to ensure seamless software-hardware integration
- Travel to customer sites to deploy, calibrate, and fine-tune robotic systems in diverse and challenging real-world conditions
- Work closely with other engineering teams to align development efforts and achieve cohesive system performance
- Communicate effectively with non-technical stakeholders to explain robotic capabilities and project progress
Skills
- Strong programming skills: Proficiency in Python and C++ for algorithm development and system integration
- Proficiency in ROS (Robot Operating System): Experience developing and integrating robotic software solutions
- Hands-On Robotics Experience: Practical experience working with autonomous robotic systems, including testing and deployment in the field
- Understanding of Planning and Controls: Solid knowledge of path planning, control theory, and trajectory optimization
- Ability and availability to travel: Ability and eagerness to travel to customer sites for deployments, testing, and troubleshooting (up to 30% travel)
- Excellent Communication Skills: Ability to convey complex technical concepts clearly to diverse audiences
- Experience with GPU programming (CUDA C++, PyTorch) a significant plus
- Experience with real-time systems or robotics middleware
- Familiarity with sensor integration and perception algorithms
- Knowledge of machine learning techniques applied to robotics
- Exposure to industrial robotics applications in sectors like construction, mining, or manufacturing
- Experience working in harsh or outdoor environments
- Experience working with humanoid or legged robots
- Advanced degree (Bachelors, Master's) in Robotics, Computer Science, Electrical Engineering, or a related field
- Understanding of safety standards and compliance in robotics
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