As a Staff VLA Engineer you'll contribute to next-generation autonomous driving intelligence research, working alongside the team to push past current VLA capabilities. You'll bring hands-on expertise in foundation models, multimodal learning, world models, or autonomous systems, and help turn research ideas into real technical progress.
ResponsibilitiesContribute 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.
- Help develop architectures that integrate reasoning, planning, memory, and action.
- Investigate driving agents capable of adaptive decision making in open-world environments.
- Contribute ideas toward future driving-agent architectures as successors to current VLA systems.
Qualifications- 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 Qualifications- 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).
- Publications at top-tier AI/ML/robotics venues (e.g., NeurIPS, ICML, ICLR, CVPR, CoRL, RSS).
- Experience with large-scale distributed training and model optimization.
- Prior experience contributing research to production systems or bringing prototypes to deployment.
- Familiarity with simulation environments for autonomous driving (e.g., CARLA, nuPlan, Waymo Open Dataset).
Interview Process- Application Review - Coding Test - 1st interview - 2nd interview - Offer Negotiation - Hiring
- The screening procedures may vary depending on the position, schedule, or other circumstances.
You will be individually notified of the screening schedule and results via the email address provided in your application.
CompensationAdditional Information- In accordance with fair hiring practices, do not include any personal information unrelated to your job qualifications (e.g., Social Security Number, family relations, marital status, age, photo, physical condition, place of birth, etc.) in your resume.
- All documents must be submitted in PDF format and under 30MB in size.
- If you experience issues uploading your resume, please send it along with the job posting URL to [redacted].
