Machine Learning Engineer (Autonomy)

About Wingfin Wingfin develops autonomous driving systems. Our mission is rooted in accessibility: human-like autonomy for safe, cost-efficient, real-world scalability. By leveraging cutting-edge deep learning and end-to-end neural networks, Wingfin is redefining the landscape of self-driving technology. We are building a practical, cost-efficient platform that brings safe autonomy to every vehicle on the road. Headquartered in Singapore, the company boasts an international team of multi-talented experts. We are looking for talented and motivated individuals to join our team either in Singapore or Malaysia. The Role This is a full-time, on-site role located in either Singapore or Kuala Lumpur for a Machine Learning Engineer (Autonomy). At Wingfin, our deep learning models provide the foundation for safe, reliable driving. However, the real world is full of "long tail" events—unstructured construction zones, complex hand signals from a traffic officer, or unique local maneuvers. While the vast majority of driving events are handled autonomously by our models with a high degree of safety and precision, remote takeover provides additional support to the models to handle extremely complex situations. As a Machine Learning Engineer (Autonomy), you will focus on the "intelligence gap" during takeover. When a human enters the loop, they need to gain situational awareness in a fraction of a second. You will develop distilled world models—efficient, high-level representations of the vehicle’s environment—that provide predictive guidance to remote operators. These models will highlight intent, predict trajectories, and surface the most critical information, enabling humans to make complex decisions at superhuman speed. Key Responsibilities You will be working on the following: Build and maintain models to enable remote operators to make complex decisions at superhuman speed. Collaborate with remote operators and control engineers to improve the collaboration experience. Ab…