Research Internship – Reinforcement Learning-Based Path Planning
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Job Description
About Mercedes-Benz Research & Development North America (MBRDNA):
MBRDNA is dedicated to developing cutting-edge automotive technologies. Their teams of engineers and designers utilize state-of-the-art software and technology to improve the driving experience and lessen environmental impact. They strive to push the boundaries of what’s possible in automotive technology. The company was recognized as one of the “Best Places to Work” by BuiltIn in January 2024, highlighting their commitment to a positive work environment that fosters diversity, collaboration, and continuous learning.
Job Description: Research Internship – Reinforcement Learning-Based Path Planning
MBRDNA is seeking a highly motivated student for a research internship focused on advancing reinforcement learning-based path planning algorithms for autonomous vehicles. The intern will be part of the autonomous driving/parking team.
Responsibilities:
• Algorithm Design and Implementation: Collaborate with the team to design and implement reinforcement learning algorithms for path planning in autonomous vehicles.
• Simulation and Validation: Develop simulation scenarios to rigorously test and validate the developed path planning strategies.
• Performance Analysis and Optimization: Analyze and optimize the performance of these algorithms across various driving and parking scenarios.
• Integration with Existing Frameworks: Assist in integrating the reinforcement learning models into existing autonomous driving/parking frameworks.
• Documentation and Presentation: Document research findings through internal reports, present results to the team, and potentially contribute to publications in top-tier conferences if results are significant.
Minimum Qualifications:
• Currently pursuing a PhD in Computer Science, Electrical Engineering, Robotics, or a related field with a strong focus on machine learning, deep learning, and reinforcement learning.
• 5+ years of relevant work/academic experience.
• Major in a relevant field (Computer Science, Electrical Engineering, Robotics) with a strong focus on machine learning, computer vision, and/or natural language processing.
• Strong understanding of reinforcement learning principles and algorithms.
• Proficiency in Python and experience with machine learning libraries (TensorFlow, PyTorch).
• Familiarity with C++.
• Familiarity with path planning techniques and autonomous driving concepts.
• Excellent problem-solving skills; ability to work independently and collaboratively.
• Strong communication skills; ability to clearly present complex technical information.
• Strong programming skills in Python and familiarity with deep learning libraries (e.g., PyTorch, TensorFlow).
Preferred Qualifications:
• Currently pursuing or recently graduated from a PhD program in a relevant field.
• Publication record in reputable AI/ML/CV/NLP conferences or journals.
• Experience with autonomous driving algorithms and systems.
Compensation: The current hourly rate is $28 (Undergraduate Students)/$32 (Graduate Students), subject to change. Full-time employee benefits (excluding interns and contractors) include medical, dental, and vision insurance; 401(k) with employer match; paid time off (unlimited for salaried employees); sick time; parental leave; tuition assistance; and wellness/fitness reimbursement programs. Eligible full-time employees may also receive a vehicle lease subsidy or company car.
