AI Research Intern (PhD)

We are seeking an AI Research Intern to join our founding team and bridge the gap between high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. In this role, you will contribute to core research initiatives on Perception, World Models and Action models. You will help translate cutting-edge theoretical breakthroughs into scalable, real-world deployment on physical autonomous systems, driving how our platforms perceive, simulate, and navigate complex environments.

Responsibilities

Integrate emerging research from the broader AI community, identifying and prototyping the most promising solutions; Design and build end-to-end multimodal models that translate real-time visual perception and high-level behavioral goals into physical vehicle actuation; Research and develop cutting-edge RL and distillation techniques for trajectory planning; Develop interactive world models from raw multi-sensor logs, allowing the team to re-simulate events and query what a vehicle would see if it altered its trajectory; Ensure core autonomous driving models can seamlessly adapt to novel urban environments and edge cases; Partner with validation and research teams to run model experiments through rigorous simulated scenarios, detecting regressions and identifying systemic performance bottlenecks

Requirements

Currently enrolled in a PhD degree program in Computer Science, EECS, Robotics, AI, or a closely related field; Publications or active submissions in top-tier AI or robotics venues (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, RSS, ICRA, CoRL); PhD level research background focused on at least one of: Generative World Models / Video Generation / Diffusion Models, Vision-Language-Action (VLA) models or embodied AI, Reinforcement Learning (RL), trajectory optimization, or decision-making systems, Large-scale multimodal Transformer architectures applied to robotics or spatio-temporal data; Advanced hands-on proficiency with PyTorch or JAX for training and fine-tuning large-scale neural networks; Proficiency in Python and working knowledge of C++

Nice to have

Prior research or internship experience in autonomous driving, mobile robotics, or spatial AI; Hands-on experience with Sensor Fusion integrating inputs across multi-camera setups, LiDAR, and Radar; Familiarity with Bird's-Eye-View (BEV) visual representations, 3D occupancy forecasting, multi-task learning, or specialized data tokenization for spatial data; Prior experience with large-scale distributed training on GPU/TPU clusters (e.g., DeepSpeed, Megatron-LM, Ray, or PyTorch FSDP)