42dot Staff VLA Engineer at 42dot contributing to next-generation autonomous driving intelligence research. Develop and prototype Vision-Language-Action architectures and support world model and driving reasoning research.
Responsibilities
Contribute to Next-Generation VLA Architectures
Research and prototype next-generation Vision-Language-Action architectures.
Explore scalable multimodal foundation models for autonomous driving.
Help develop architectures with improved generalization, reasoning, and long-horizon decision making.
Support work on large driving models that unify perception, planning, and action generation.
Support World Model & Driving Reasoning Research
Build and experiment with world-model-based approaches for predictive driving intelligence.
Work on future-state prediction, behavior forecasting, and counterfactual simulation.
Contribute to long-horizon planning and reasoning research for autonomous driving.
Help build models that understand complex traffic interactions and latent agent intentions.
Explore Agentic Driving Systems
Research goal-driven autonomous driving agents.
Qualification
MS or PhD in Computer ScienceAbility to readExperience with world modelsInterview Process
Required
MS or PhD in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field (or equivalent practical experience).
Strong hands-on experience with deep learning frameworks (e.g., PyTorch, JAX).
Solid understanding of foundation models, multimodal learning, or transformer-based architectures.
Experience training or fine-tuning large-scale models (vision, language, or multimodal).
Strong software engineering skills and experience working with large-scale data pipelines.
Ability to read, implement, and extend ideas from recent AI research papers.
Excellent collaboration and communication skills, comfortable working across distributed, cross-functional, and cross-cultural teams (Silicon Valley + HQ Korea).
Preferred
Research experience in autonomous driving, robotics, or embodied AI (e.g., perception, planning, control, or end-to-end driving models).
Experience with world models, model-based reinforcement learning, or predictive/generative simulation.
Familiarity with Vision-Language-Action (VLA) models or agentic AI architectures (reasoning, planning, memory, tool use).
