Atoms AI/ML Research Intern contributing to research on World Models and Vision-Language-Action models for autonomous transport platforms. Responsible for developing RL techniques and prototyping multimodal models for physical vehicle actuation.
Responsibilities
We are seeking a visionary AI/ML 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.
Research and develop cutting-edge RL and distillation techniques for trajectory planning.
Integrate emerging research from the broader AI community, identifying and prototyping the most promising solutions.
Design and prototype end-to-end multimodal models that translate real-time visual perception and high-level behavioral goals into physical vehicle actuation.
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.
Education: Currently enrolled in a PhD degree program in Computer Science, EECS, Robotics, Machine Learning, or a closely related quantitative field.
Research Track Record: Publications or submissions in top-tier machine learning or robotics venues (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, RSS, ICRA, CoRL).
Core Expertise: Graduate-level research background focused on at least one of the following domains:
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
Framework Fluency: Advanced hands-on proficiency with PyTorch or JAX for training and fine-tuning large-scale neural networks.
Programming: Proficiency in Python and working knowledge of C++.
Preferred
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).
Prior research or internship experience in autonomous driving, mobile robotics, or spatial AI.
