Senior Machine Learning Engineer, Perception

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.

Design, implement, and optimize BEV-based perception models that fuse camera, LiDAR, and radar inputs. Benchmark perception models using large-scale datasets and well-defined quantitative metrics. Collaborate cross-functionally with research, data, and deployment engineers to refine models and support real-world applications. Maintain a strong focus on performance, robustness, and scalability for deployment in production systems. Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts. Ensure team compliance with QMS, monitor quality, and drive process improvements.

Ph.D. or Masters in AI, Computer Science, Electrical Engineering, Robotics, or a related field. Ph.D. new grad or Masters + 3 years industry experience Proficiency in Python and experience building deep learning pipelines. Strong expertise in PyTorch, TensorFlow, or JAX. Proven experience with LiDAR-based 3D perception and BEV representation models Deep understanding of multimodal sensor fusion architectures and techniques. Familiarity with camera, LiDAR, and radar modalities and their synchronization, calibration, and integration in perception pipelines. Solid foundation in computer vision, deep learning, and 3D geometry.

Industry or academic experience in autonomous vehicle perception, robotics, or related areas. Hands-on experience developing deep learning models in real-world or production environments. Experience with distributed training, high-performance computing, or GPU acceleration.