Robotics AI Scientist – Autonomy & Decision Systems

Job Title: Robotics AI Scientist – Autonomy & Decision Systems
Location: Manhattan, NY (On-Site Preferred, Hybrid Considered)

About the Company:
Our client is redefining automation at the edge of robotics and artificial intelligence. Operating at the intersection of machine learning, advanced robotics, and real-world applications, they are building intelligent platforms capable of autonomous operations across diverse and unstructured environments. With cutting-edge projects spanning manufacturing, biotech, and autonomous systems, this organization is committed to unlocking the future of physical AI in everyday operations.

Position Overview:
We are seeking a Robotics AI Scientist with a passion for applied research and a drive to build systems that think, learn, and adapt in real-time. This role combines groundbreaking research in areas such as reinforcement learning and probabilistic reasoning with hands-on deployment into hardware-rich environments. The ideal candidate thrives at the crossroads of research and engineering, and is motivated by the opportunity to see their work come to life through autonomous, intelligent machines.

Key Responsibilities:

• Lead development of novel AI models and algorithms for robotic perception, planning, and control.
• Investigate and apply methodologies in reinforcement learning, imitation learning, and probabilistic modeling.
• Collaborate cross-functionally with roboticists and ML teams to validate and deploy algorithms in real-world conditions and high-fidelity simulation environments.
• Publish cutting-edge research in top-tier academic venues and contribute to the global AI and robotics community.
• Translate experimental findings into production-grade AI modules for robotic platforms.
• Drive the creation of simulation and benchmarking environments to enhance model robustness and test new capabilities.
• Shape the organization’s strategic roadmap for AI advancement in autonomous systems.

Core Focus Areas May Include:

• Continual learning and adaptation in physical systems
• Vision-language integration for robotic interaction
• Safety-aware planning under uncertainty
• Learning from demonstration and human-in-the-loop reinforcement
• Sim-to-real transfer in complex robotics environments
• Multi-agent autonomy and coordination

Qualifications:

Required:

• PhD in Artificial Intelligence, Robotics, Computer Science, or equivalent research experience
• Demonstrated publication record in AI or robotics conferences (e.g., NeurIPS, ICRA, RSS, CVPR)
• Deep expertise in areas such as reinforcement learning, planning, probabilistic reasoning, or embodied AI
• Proficiency with Python and major ML frameworks (e.g., PyTorch, TensorFlow, JAX)
• Hands-on experience with robotic systems, simulators (e.g., Gazebo, MuJoCo, Isaac), or real-world deployments

Preferred:

• Familiarity with robotic control stacks and ROS/ROS2
• Background in embedded ML and edge-deployment constraints
• Contributions to open-source or experience mentoring junior researchers
• Exposure to perception stacks and vision-based learning in robotics

What’s in It for You:

• Be a catalyst in shaping the next generation of autonomous systems and embodied AI
• Access to advanced robotics platforms, compute infrastructure, and real-world datasets
• Competitive compensation, equity opportunities, and comprehensive benefits
• Support for continued learning, conference travel, and research publications
• A collaborative, technically rigorous team environment driving frontier innovation