Robotics AI Scientist | Remote

Crossing Hurdles; United States; On-site; USD 160,000 - 260,000; Full time

Job summary

Crossing Hurdles is seeking a senior researcher to define and lead the Physical AI agenda across robotics, embodied agents, and real-world automation. You will develop intelligent agents that connect foundation models with robots, machines, and physical systems, and guide research in robot learning, RL/IL, and world models.

You will design data-generation strategies, build scalable datasets and simulation environments, and establish rigorous evaluation frameworks for cross‑environment transfer.

Qualifications

• Strong experience in robotics, embodied AI, robot learning, autonomous systems, or related research fields.
• Deep understanding of robot learning, multimodal models, reinforcement learning, imitation learning, planning, and control.
• Strong knowledge of sensing, calibration, actuation, hardware constraints, uncertainty, and physical system integration.
• Demonstrated research excellence in identifying high-impact problems and developing innovative technical solutions.
• Strong experimental skills in hypothesis development, controlled testing, benchmarking, and evidence-based analysis.
• Expertise in evaluation methodologies, generalization, simulation-to-reality transfer, and robotics system design.
• Proven research leadership with the ability to mentor technical talent and lead ambitious research programs; a Master s or PhD in a relevant field is preferred.

Responsibilities

• Define and lead the Physical AI research agenda across robotics, embodied agents, learning, evaluation, and physical-world automation.
• Develop intelligent agents that connect foundation models with robots, machines, instruments, and physical systems.
• Lead research in robot learning, vision-language-action models, imitation learning, reinforcement learning, and world models.
• Design data-generation strategies using demonstrations, sensor data, simulation, teleoperation, and autonomous interactions.
• Develop scalable datasets, simulation environments, and infrastructure to improve physical-world learning and generalization.
• Establish rigorous evaluation frameworks for manipulation, planning, spatial reasoning, robustness, safety, and cross-environment transfer.
• Recruit, mentor, and lead exceptional researchers and engineers while collaborating with leading AI and robotics research organizations.

Skills

Robotics

Embodied AI

Robot learning

Autonomous systems

Research leadership

Education

Master's or PhD in a relevant field

Tools

ROS

Job description

• Define and lead the Physical AI research agenda across robotics, embodied agents, learning, evaluation, and physical-world automation.
• Develop intelligent agents that connect foundation models with robots, machines, instruments, and physical systems.
• Lead research in robot learning, vision-language-action models, imitation learning, reinforcement learning, and world models.
• Design data-generation strategies using demonstrations, sensor data, simulation, teleoperation, and autonomous interactions.
• Develop scalable datasets, simulation environments, and infrastructure to improve physical-world learning and generalization.
• Establish rigorous evaluation frameworks for manipulation, planning, spatial reasoning, robustness, safety, and cross-environment transfer.
• Recruit, mentor, and lead exceptional researchers and engineers while collaborating with leading AI and robotics research organizations.

Requirements

• Strong experience in robotics, embodied AI, robot learning, autonomous systems, or related research fields.
• Deep understanding of robot learning, multimodal models, reinforcement learning, imitation learning, planning, and control.
• Strong knowledge of sensing, calibration, actuation, hardware constraints, uncertainty, and physical system integration.
• Demonstrated research excellence in identifying high-impact problems and developing innovative technical solutions.
• Strong experimental skills in hypothesis development, controlled testing, benchmarking, and evidence-based analysis.
• Expertise in evaluation methodologies, generalization, simulation-to-reality transfer, and robotics system design.
• Proven research leadership with the ability to mentor technical talent and lead ambitious research programs; a Master s or PhD in a relevant field is preferred.