Machine Learning Intern, Perception (End-to-end)

We are looking for a Machine Learning Research Intern to work on end-to-end autonomous driving, with a focus on learning-based models that connect perception, reasoning, prediction, and action. The research direction may include Vision-Action models, Vision-Language-Action models, world models, world-action models, and other foundation-model-inspired approaches for autonomous driving. This role involves literature review, model prototyping, experiment design, evaluation, and analysis.

The internship will take place in our Singapore office and we expect a full-time internship period of at least 5 months. We offer flexible working hours and allow for remote work. The candidate will however have to live in Singapore for the duration of the internship.

Responsibilities:

• Conduct research on end-to-end autonomous driving models, including Vision-Action models, Vision-Language-Action models, world models, world-action models, and related approaches
• Explore learning-based approaches that connect perception, scene understanding, future prediction, decision-making, and driving action generation
• Prototype, train, and evaluate models using multi-modal autonomous driving data, including images, videos, LiDAR, radar, maps, ego-motion, trajectories, and driving logs
• Investigate different learning paradigms, such as imitation learning, reinforcement learning, generative modeling, or hybrid learning-based planning
• Analyze model performance, robustness, generalization, and failure cases in complex driving scenarios
• Document research ideas, model designs, experiment results, and key findings in clear and reproducible formats
• Contribute to technical reports, invention disclosures, patent applications, and research paper writing and submission

Required Skills:

• Currently pursuing a Master’s or PhD degree in Computer Science, Machine Learning, Robotics, Electrical Engineering, or a related field
• Research experience in at least one of the following areas: computer vision, sequence modeling, imitation learning, reinforcement learning, robotics, planning, or autonomous driving
• Proficient in Python and/or C++
• Proficient with deep learning frameworks such as PyTorch or TensorFlow
• Experience conducting research-oriented experiments, including model training, evaluation, ablation studies, and result analysis
• Experience working with structured or unstructured data, such as images, videos, point clouds, trajectories, maps, or sensor logs
• Strong problem-solving skills and ability to communicate research ideas and experimental findings clearly

Preferred Skills:

• Research or project experience in autonomous driving, robotics, embodied AI, or learning-based planning
• Experience with end-to-end autonomous driving, Vision-Action models, Vision-Language-Action models, world models, or world-action models
• Familiarity with imitation learning, reinforcement learning, or generative modeling
• Familiarity with autonomous driving datasets, benchmarks, or simulators such as nuScenes, Waymo Open Dataset, Argoverse, NAVSIM, CARLA, or related frameworks
• Experience with large-scale model training, distributed training, or efficient fine-tuning
• Publications, open-source contributions, or strong research projects in relevant areas are a plus