Job Details:

Job Description:

Responsibilities • Participate in the research and development of cutting-edge Robotics Embodied AI algorithms. • Assist in the development and training of Vision-Language-Action Models (VLA) based robotics control strategies, converting multi-modal data (images, video, text, haptics, etc.) into robot decision-making. • Research and implement Reinforcement Learning (RL) algorithms for robot skill acquisition and decision processes, particularly for dexterous manipulation and navigation tasks. • Explore and apply Model Predictive Control (MPC) or related control theory methods to optimize motion planning and low-level control performance in complex environments. • Participate in the development and maintenance of robot simulation environments (e.g., Isaac Sim, MuJoCo, PyBullet), and be responsible for successfully transferring algorithms trained in simulation to real robot hardware platforms (Sim2Real). • Analyze experimental data, evaluate algorithm performance, and write technical reports and relevant academic papers. • Collaborate closely with the team to jointly advance the development and application of embodied intelligence frontier technologies. • Excellent problem-solving abilities, data analysis skills, and teamwork spirit. • Good English communication and learning skills.

Qualifications:

Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Minimum Qualifications: • Currently pursuing a Master's or Ph.D. degree (Computer Science, Robotics, Artificial Intelligence, Automation, Electrical Engineering, or related fields); outstanding undergraduate students may also be considered. • Solid programming skills, proficient in Python, familiarity with C++ is a plus. • Proficient in using at least one deep learning framework, such as PyTorch, TensorFlow, etc. • Strong interest in and deep understanding of the field of robotics embodied AI, with relevant research or project experience. Preferred Qualifications: • Experience applying VLA (Vision-Language-Action) models or large multi-modal models in robotics. • Research experience in Reinforcement Learning (RL) or Imitation Learning algorithms. • Practical experience with Model Predictive Control (MPC) or classic control theory in robotics control. • Experience using Robot Operating System (ROS); hands-on experience debugging real robot hardware is a plus. • Published papers in relevant top-tier conferences/journals (e.g., ICRA, IROS, CoRL, NeurIPS, ICML, CVPR, etc.). Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research.

          

Job Type:

Student / Intern

Shift:

Shift 1 (China)

Primary Location:

PRC, Shanghai

Additional Locations:

Business group:

The Sales and Marketing Group (SMG) leverages the product portfolio to drive Intel's revenue growth and market expansion, blending strategic initiatives with dynamic sales efforts to capture and retain customers. SMG is responsible for empowering the sales force with tools and insights needed to close deals and build lasting customer relationships. Sales analytics and market research ensure strategies are both targeted and impactful. In SMG, disciplined execution, creativity, and ambition are celebrated, providing ample opportunities for career advancement and skill development.

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust

N/A

Work Model for this Role

This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.