Job Description

Responsibilities:

• 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.

Required Skills:

• 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.

Preferred Skills:

• 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.