JOB DESCRIPTION

About NIO

NIO is a pioneer and a leading company in the premium smart electric vehicle market. Founded in November 2014, NIO’s mission is to shape a joyful lifestyle. NIO aims to build a community starting with smart electric vehicles to share joy and grow together with users.

NIO designs, develops, jointly manufactures and sells premium smart electric vehicles, driving innovations in next-generation technologies in autonomous driving, digital technologies, electric powertrains and batteries. NIO differentiates itself through its continuous technological breakthroughs and innovations, such as its industry-leading battery swapping technologies, Battery as a Service, or BaaS, as well as its proprietary autonomous driving technologies and Autonomous Driving as a Service, or ADaaS.

NIO’s product portfolio consists of the ES8, a six-seater smart electric flagship SUV, the ES7 (or the EL7), a mid-large five-seater smart electric SUV, the ES6, a five-seater all-round smart electric SUV, the EC7, a five-seater smart electric flagship coupe SUV, the EC6, a five-seater smart electric coupe SUV, the ET7, a smart electric flagship sedan, and the ET5, a mid-size smart electric sedan.

We are building next-generation dexterous manipulation intelligence for embodied robotics systems. Our work spans contact-rich manipulation, physics-based simulation, and scalable data generation for robotic learning systems.

This internship will focus on advancing our high-fidelity simulation infrastructure to support contact-rich robotic manipulation.

Project Scope

The intern will contribute to one or more of the following areas:

High-Fidelity Contact Simulation

• Improve geometric modeling and mesh processing pipelines for robotic hands and objects.
• Develop robust surface reconstruction and mesh conditioning tools for simulation assets.
• Analyze mesh quality, collision stability, and contact robustness.

Physics-Driven Simulation Infrastructure

• Design automated pipelines for physics parameter identification (System ID) to calibrate contact dynamics (e.g., stiffness, damping, friction profiles).
• Develop tools for systematic sensitivity analysis and domain randomization of simulation parameters.
• Build robust simulation wrappers and configuration modules to manage contact-rich environments across different backend solvers (e.g., MuJoCo, Isaac).

Rendering & Visualization for Simulation Debugging

• Build real-time, high-performance visualization tools for contact points, contact forces, friction cones, and constraint violations.
• Develop zero-copy, tensor-native GPU debugging overlays to inspect massively parallel simulations without bottlenecking data generation throughput.
• Design intuitive UI/UX for robotics researchers to toggle and filter complex contact interactions during live policy rollouts.

Simulation-to-Real Gap Analysis

• Design controlled experiments to measure contact dynamics fidelity by comparing simulation trajectories against real-world hardware logs.
• Evaluate simulation robustness under sim-to-real transfer conditions:
  • Contact state perturbations and sensor noise
  • Imperfect object meshes (scanned vs. ground truth)
  • Physical parameter domain randonmization
• Produce quantitative reports to guide the calibration of our physical simulation infrastructure.

Roles and Responsibilities

• Massively parallel GPU simulation architecture and scalable infrastructure for robot learning.
• Advanced contact mechanics and numerical methods for contact-rich robotics.
• System Identification (System ID) and practical sim-to-real transfer techniques.
• Industry-scale research execution for embodied foundation models.

• Production-quality, well-documented simulation tools, wrappers, or mesh processing modules integrated into our codebase.
• A comprehensive technical report documenting:
  • Experimental design (e.g., for System ID or stress-testing).
  • Evaluation methodology for contact stability and the sim-to-real gap.
  • Quantitative benchmarking results.
• An internal presentation to the robotics team demonstrating the new tools during live policy rollouts.

Qualifications

• PhD or strong MS student in Computer Science, Robotics, Computer Graphics, or related field.
• Strong C++ and Python programming skills.
• Solid foundation in 3D geometry processing, mesh generation, or surface reconstruction.
• Experience with rendering APIs (OpenGL/WebGL/Vulkan) and GPU programming.
• Strong debugging skills and system-level thinking.

Preferred Qualifications

• Hands-on experience with robotics simulators (MuJoCo, Isaac, Bullet, etc.).
• Knowledge of collision detection algorithms (e.g., GJK, EPA) and contact modeling (LCP, soft contacts).
• Experience with CUDA, parallel computing, or tensor-native operations (e.g., PyTorch).
• Familiarity with writing Python bindings for C++ code (e.g., pybind11) and integrating them into ML pipelines.
• Prior experience working with real robot hardware or sim-to-real transfer pipelines.

Compensation:

The US base salary range for this full-time position is $38.00 - $46.00.

• Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

• Please note that the compensation details listed in US role postings reflect the base salary only. It does not include discretionary bonus, equity, or benefits.