Research Scientist Intern

About the Role

Key Responsibilities

• Conduct research on designing and implementing large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.
• Design and integrate cross-modal alignment (e.g., visual grounding, temporal reasoning, policy distillation, imitation and reinforcement learning) to improve model interpretability and action quality.
• Closely collaborate with researchers and engineers across the modeling and infrastructure team.
• Contribute to top-tier AI/CV/ML conferences publications and present research findings.

Minimum Qualifications

• Currently enrolled in the Master/Ph.D program in Computer Science, Electrical/Computer Engineering, or related field, with the specialization in the CV/NLP/ML.
• Experience in multi-modal modeling (vision, language, or planning), with deep understanding of representation learning, temporal modeling, and reinforcement learning techniques.
• Strong proficiency in PyTorch and modern transformer-based model design.

Preferred Qualifications

• Publication record in top-tier AI conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, etc).
• Prior experience building foundation or end-to-end driving models, or LLM/VLM architectures (e.g., ViT, Flamingo, BEVFormer, RT-2, or GRPO-style policies).
• Knowledge of RLHF/DPO/GRPO, trajectory prediction, or policy learning for control tasks.
• Familiarity with distributed training (DDP, FSDP) and large-batch optimization.

What We Offer

• A collaborative, research-driven environment with access to massive real-world data and industry-scale compute.
• Opportunity to work with top-tier researchers and engineers advancing the frontier of foundation models for autonomous driving.
• Direct impact on the next generation of intelligent mobility systems.
• Comprehensive benefits, meals, and team-building activities.