Robotics Engineering Intern: Sim-to-Real Autonomy

Intuitive Surgical, Inc.; Sunnyvale (CA); Presencial; USD 85 000 - 113 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Housing allowance

Resumo da oferta

Intuitive Surgical, Inc. seeks a Robotics Engineering Intern to help develop a sim-to-real-to-sim platform for the robotic surgical system.

You will enable rapid prototyping and validate autonomous controllers using simulation and hardware tests at our Sunnyvale, CA site. The role focuses on transferring controllers from simulation to robot hardware, building virtual models of the surgical field, and iterating between simulation and reality to accelerate development and evaluation of autonomous

Qualificações

• Currently enrolled in a robotics, electrical, mechanical, computer science, or related degree program.
• Must be able to work full-time for 10–12 weeks starting May or June.
• Enrollment must be in a Master’s or Doctorate program.
• Eligible for university hiring and internship program.

Responsabilidades

• Implement novel control architectures in high- and low-level targets.
• Test controller performance in simulation and on hardware.
• Create simulation environments with constrained dynamics and realistic rendering.
• Synthesize simulated environments from video data.
• Conduct closed-loop tests: train in simulation, test, and update simulations.

Conhecimentos

MATLAB

Python

Autonomous control

Embedded systems

Formação académica

Master’s or PhD in Robotics / EE / CS

Ferramentas

MuJoCo

NVIDIA Isaac Sim

Embedded C++

Descrição da oferta de emprego

Intuitive Surgical, Inc. seeks a Robotics Engineering Intern to help develop a sim-to-real-to-sim platform for the robotic surgical system.

You will enable rapid prototyping and validate autonomous controllers using simulation and hardware tests at our Sunnyvale, CA site. The role focuses on transferring controllers from simulation to robot hardware, building virtual models of the surgical field, and iterating between simulation and reality to accelerate development and evaluation of autonomous