Intern, AD Perception

At Mercedes-Benz Research & Development North America (MBRDNA), we are committed to delivering world-class automotive technologies that push the boundaries of what is possible. Our teams of highly skilled engineers and designers use cutting-edge software and technology, to enhance the driving experience and reduce environmental impact. 
We are seeking a highly motivated research intern to join our AD Perception team, focusing on scene understanding and generative modeling for autonomous driving. This role is ideal for candidates with a strong research background who are eager to apply their expertise in machine learning to real-world challenges. You will work closely with a multidisciplinary team of researchers and engineers to develop innovative models and algorithms that enhance our perception stack. 

Job Responsibilities:

• Develop and optimize ML models for scene understanding, map fusion, and scene graph generation.
• Develop approaches for automated map generation from sensor data.
• Research methods to fuse map priors with perception data to build an accurate environment model in real-time.
• Integrate foundation models into the perception and mapping system.
• Work with large-scale multi-modal datasets (camera, LiDAR, radar) and enhance fusion approaches.
• Present research findings and contribute to publications or internal reports.

Minimum Qualifications:

• MS degree in Major in Computer Science, Electrical Engineering, Robotics, or a related field, with a strong focus on machine learning, computer vision, and/or natural language processing etc)
• MS degree (currently pursuing PhD) in Computer Science, Electrical Engineering, Robotics, or a related field, with a strong focus on machine learning, computer vision, and/or natural language processing.
• Demonstrated experience in developing and training deep learning models, particularly in 3D scene understanding, map-based perception, or scene graph generation.
• Solid understanding of state-of-the-art vision and language models (e.g., CLIP, BLIP, VLM adaptions of ViT, LLM-integrated frameworks).
• Strong programming skills in Python and familiarity with deep learning libraries (e.g., PyTorch, TensorFlow).
• Masters + 3 yrs / PhD or Minimum years of experience required 5 years

Preferred Qualifications:

• Currently pursuing or recently graduated from a PhD program in Computer Science, Electrical Engineering, Robotics, or a closely related discipline
• Publication record in reputable AI/ML/CV/NLP conferences or journals
• Experience with Autonomous Driving algorithms and systems

Benefits/Perks:•  PTO• Sick Time
Additional Information:The current hourly rate for this position is as follows and may be modified in the future: $28/hr (Undergraduate Students)/$32/hr (Graduate Student)
Why should you apply?Here at MBRDNA, you create digital ecosystems around cars, you design a language between humans and machines, you make a car even more intelligent - you make the new reality for cars. MBRDNA was honored as one of the "Best Places to Work" by BuiltIn in January 2024, a testament to our commitment to creating an exceptional work environment. At each of our offices, we foster a culture of collaboration and continuous learning, ensuring every team member can thrive and innovate.
Benefits for Full-Time* Employees Include: • Medical, dental, and vision insurance for employees and their families • 401(k) with employer match • Up to 18 company-paid holidays • Paid time off (flexible time off for salaried employees), sick time, and parental leave • Tuition assistance program • Wellness/Fitness reimbursement programs * Internships & Contractors excluded from Full-Time Employee benefits

MBRDNA is an equal opportunity employer (EOE) and strongly supports diversity in the workforce. MBRDNA only accepts resumes from approved agencies who have a valid Agency Agreement on file. Please do not forward resumes to our applicant tracking system, MBRDNA employees, or send to any MBRDNA location.  MBRDNA is not responsible for any fees or claims related to receipt of unsolicited resumes.
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