Cssmerge AI Research Intern to bridge AI research and real-world physical actuation for autonomous transport platforms. Contribute to research on Perception, World Models, and Action models and translate breakthroughs into scalable deployment.
Responsibilities
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.
Integrate and adapt emerging AI research into internal model pipelines, validating performance on real-world datasets and simulation.
Prototype and evaluate RL, trajectory planning, and distillation algorithms based on state-of-the-art literature.
Train and fine-tune multimodal models that map real-time sensor inputs and behavioral goals to vehicle actuation.
Experiment with world model architectures using multi-sensor log data to improve simulation fidelity and trajectory querying.
Analyze edge cases and failure modes in novel driving environments to iterate on model performance and stability.
Run rigorous simulation experiments alongside research and validation teams to identify performance bottlenecks and track model improvements.
Qualification
EducationCore ExpertiseReinforcement Learning (RL)Framework FluencyProgrammingPrior research
Required
Education: Currently enrolled in a Bachelor's or Master's degree program in Computer Science, EECS, Robotics, AI, or a closely related field.
Core Expertise: Coursework, project experience, or 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
Prior research, project, or internship experience in autonomous driving, mobile robotics, or spatial AI.
