Deep Learning Research Intern (Multimodal BEV Perception)

• We are seeking a highly motivated Deep Learning Research Intern to join our Perception team, working on Bird’s Eye View (BEV) fusion models using multimodal sensor inputs, including camera, LiDAR, and radar
• You will support the design and evaluation of scalable perception algorithms that enable 3D scene understanding for autonomous driving, working closely with research engineers on real-world, production-relevant problems
• Research, prototype, and evaluate BEV-based perception models that fuse camera, LiDAR, and radar inputs
• Design and run experiments benchmarking model performance on large-scale autonomous driving datasets using well-defined quantitative metrics
• Investigate novel multimodal fusion architectures and techniques to improve accuracy, robustness, and efficiency
• Collaborate closely with research scientists and perception engineers to translate findings into production-ready components
• Document experiments, results, and insights clearly for the broader research team
• Perform work in accordance with the company’s Quality Management System (QMS) requirements where applicable

Benefits

• Unlimited PTO: Unlimited paid time off on top of company holidays to recharge and enjoy as needed
• Flexible working: Flexibile work arrangements and technology to help manage life’s demands at the office and remote
• Health benefits: Tiered options in medical, dental, and vision insurance to fit your unique needs
• Future building: Equity, 401k and FSA options for all eligible employees to help build for your future
• Professional development: Company-sponsored professional development opportunities such as conferences, training, and workshops
• Catered lunches: Daily catered lunches at both of our Santa Clara and Fremont offices- Understanding of multimodal sensor fusion concepts
• Hands-on experience with PyTorch, TensorFlow, or JAX
• 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
• Solid foundation in computer vision, deep learning, and 3D geometry
• Coursework or project experience with LiDAR-based 3D perception or BEV representation models
• Familiarity with camera, LiDAR, and radar modalities, including synchronization, calibration, and integration in perception pipelines
• Prior research or project experience in autonomous vehicle perception, robotics, or related fields
• Experience with distributed training, high-performance computing, or GPU acceleration
• Publications or open-source contributions in perception, 3D vision, or multimodal learning