Intern, AD Perception

As an Intern in AD Perception at Mercedes-Benz Research & Development North America (MBRDNA), you will engage in innovating machine learning models for scene understanding, map fusion, and scene graph generation. Additionally, you will research methods to automate map generation from sensor data, fuse map priors with real-time perception data, and integrate foundation models within our perception systems, ultimately enhancing our autonomous driving capabilities.

To qualify for the AD Perception Intern role at Mercedes-Benz Research & Development North America, you should be pursuing or have completed a Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field, focusing on machine learning, computer vision, or natural language processing. Proven experience developing deep learning models for 3D scene understanding, strong programming skills in Python, and familiarity with frameworks like PyTorch and TensorFlow are also essential.

During your internship with Mercedes-Benz Research & Development North America in AD Perception, you will work on pioneering projects that involve optimizing machine learning models for real-time environment modeling, automating map generation using sensor data, and enhancing our perception stack that combines data from various sources like cameras and LiDAR. This hands-on experience will be invaluable in your career!

While not mandatory, preferred qualifications for the Intern position in AD Perception at Mercedes-Benz Research & Development North America include being a recent PhD candidate or graduate in a relevant field and having a publication record in notable AI/ML/CV/NLP journals. Experience with autonomous driving algorithms and systems will also give you a competitive edge while applying for this internship.

While internships at Mercedes-Benz Research & Development North America do not offer full-time employee benefits, they do provide valuable work experience, the opportunity to work with leading experts in the field, and a supportive work environment that encourages continuous learning and professional growth. The hourly rate for this intern role starts at $28/hr for undergraduate students and $32/hr for graduate students.

When answering this question, highlight specific projects where you developed or optimized machine learning models for scene understanding, particularly in relation to autonomous driving. Discuss the techniques and algorithms you used, such as convolutional neural networks (CNNs), and how they contributed to enhancing scene perception.

Be prepared to mention your proficiency in frameworks like PyTorch and TensorFlow. Explain specific projects or tasks where you utilized these tools effectively, detailing any challenges you faced and how you overcame them.

Emphasize your understanding of integrating diverse data sources such as cameras, LiDAR, and radar. Discuss methods you've used or learned about for effective data fusion, and share any relevant experiences you have had with multi-modal datasets in past projects.

Identify key challenges such as data noise, sensor limitations, and real-time processing requirements. Explain your problem-solving strategies, perhaps including the use of filtering techniques or robust algorithms that you've learned or researched.

Share details about a specific project where your research led to a publication, focusing on the objective of the study, your contributions, and the outcomes. Highlight how this experience has prepared you for publishing future findings.

Talk about models like CLIP, BLIP, and ViT, and your insights into their applications in computer vision and natural language processing. If possible, relate how this knowledge could apply to projects at Mercedes-Benz.

Discuss any relevant projects where you've utilized Python for ML purposes. Highlight how programming in Python is crucial for implementing algorithms and libraries for research and development.

Reflect on what fascinates you about autonomous driving and how it relates to broader impacts on society, safety, and technology. Your answer can showcase your passion for the field and how it aligns with the goals of MBRDNA.

Mention specific resources including journals, conferences, and online communities you actively follow to stay informed about the latest trends, research findings, and tools in the field of machine learning and computer vision.

Articulate the technical skills necessary for AD Perception, such as strong analytical and programming skills, together with soft skills like teamwork, communication, and adaptability to thrive in a collaborative research environment.