WoRV Team is Hiring!

Job Introduction - Robotics AI Researcher

“A role focused on researching and developing robot intelligence on par with human intelligence.”

The WoRV (World model for Robotics and Vehicle control) team tackles robotics and autonomous‑driving challenges by creating agents that, like humans, integrate diverse, high‑dimensional information to perceive, decide, and act.

As a Robotics AI Researcher, you will work with CANVAS to progressively replace legacy, modular, rule‑based components of autonomous driving (Perception, Planning, Control, Localization, Mapping) with new methodologies, unifying them into a single model to achieve human‑level, intuitive, and intelligent driving.

Research Support

• Ultra‑High‑Performance GPU Cluster: We maintain CORE (Compute‑Oriented Research Environment) CORE Introduction
  • On‑premise DGX H100: 12 units (H100×96), over 30 A100 GPUs, and more than 10 V100 GPUs in operation.
• High‑Quality, Fully Human‑Driven Driving Data Pipeline for both simulation and real‑world environments
  • Unlimited access to custom datasets and annotations tailored to the research team's needs.
• Extensive Map‑Based Driving Dataset
  • Over 200 hours of driving data available, with unlimited access for the research team.
• Comprehensive Conference & Publication Support
  • Assistance with attending and submitting to NeurIPS, ICLR, CVPR, ECCV, Interspeech, ACL, etc.
  • Full coverage of conference participation expenses upon paper acceptance in international journals.

For more details:

• Publications | maum.ai BRAIN Team
• Open-Source Activities | maum.ai BRAIN Team

Key Responsibilities

• Gradually replace legacy, modular, rule‑based autonomous driving components (Perception, Planning, Control, Localization, Mapping) with novel approaches
• Conduct training, experimentation, and and testing in both simulated and real‑world environments

Required Skills

• Strong foundational knowledge and engineering skills in Computer Science:
  • Comprehensive understanding and solid basics in disciplines such as operating systems, data structures, algorithms, databases, and networks.
• Basic understanding of and interest in machine learning and deep learning.
• A proactive attitude toward growth, learning, and sharing knowledge with others.
• Proactive attitude toward growth, learning, and knowledge sharing
• Strong interest in tackling real‑world problems in the robotics domain

Preferred Qualifications

• Experience in research and development across the robotics field, specifically in navigation and SLAM
• Experience in research and development related to Imitation Learning and Reinforcement Learning
• Experience in research and development of LLMs and MLLMs (Multimodal LLMs)
• Awards or distinctions in STEM fields (e.g., Olympiads in computer science, mathematics, physics)
• Ability to quickly follow and adapt to emerging research trends
• Strong research track record in machine learning and robotics (publications in major conferences and journals)

Recruitment Process

• Application Review → Coding Test → Pre-assignment → In-Person Interview → Offer Negotiation → Final Acceptance
  • Coding tests primarily feature fundamental data structure and algorithm questions.

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How to Apply

• Apply via Google Form

• [email protected]

  • You can submit your resume (and portfolio) through either method, whichever is convenient for you. </aside>

• Applicants will be notified of the results of the application review within three days. We appreciate your understanding in advance.

  • The coding test and in-person interview will be scheduled to accommodate the applicant’s convenience as much as possible.

• There are no strict requirements for the format of the submitted documents (resume/portfolio).

  • However, please ensure the file format is PDF!

• For any questions regarding recruitment, feel free to reach out via [email protected] or LinkedIn DM.

Additional Notes

• If any false information is found in the application, the offer may be revoked.
• If necessary, and with the applicant’s consent, reference checks may be conducted.
• For full-time positions, there is a three-month probationary period, during which the evaluation results may lead to an extension of the probation or cancellation of the employment offer.