Deep Learning Research Intern — Multimodal BEV Perception
On-siteSanta Clara, California, United States
EXPIREDSanta Clara, California, United StatesOn-siteInternshipEntry LevelDoctorate Or Professional DegreeMedium
InternshipEntry LevelDoctorate Or Professional DegreeMedium
Job Summary
Research, prototype, and evaluate BEV-based perception models that fuse camera, LiDAR, and radar inputs to enable 3D scene understanding for autonomous driving. Design experiments benchmarking model performance on large-scale datasets using quantitative metrics, while investigating novel multimodal fusion architectures to improve accuracy, robustness, and efficiency. Collaborate with research scientists and perception engineers to translate findings into production-ready components and document results clearly. Perform work in accordance with the company's Quality Management System (QMS) requirements.
Required Qualifications
- Currently pursuing a Ph.D. or Master's degree in AI, Computer Science, Electrical Engineering, Robotics, or a related field
- Strong programming skills in Python and experience building deep learning pipelines
- Hands-on experience with PyTorch, TensorFlow, or JAX
- Solid foundation in computer vision, deep learning, and 3D geometry
- Coursework or project experience with LiDAR-based 3D perception or BEV representation models
- Understanding of multimodal sensor fusion concepts
Desired Qualifications
- Prior research or project experience in autonomous vehicle perception, robotics, or related fields
- Familiarity with camera, LiDAR, and radar modalities, including synchronization, calibration, and integration in perception pipelines
- Experience with distributed training, high-performance computing, or GPU acceleration
- Publications or open-source contributions in perception, 3D vision, or multimodal learning
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