What are the responsibilities and job description for the Machine Learning Intern position at Mercedes-Benz R&D North America?
The Mercedes-Benz AI Experiences team is seeking a Machine Learning Engineering Intern to join our ML Product Development Team for the Summer of 2025. You will work with our Mercedes-Benz Research & Development North America (MBRDNA) team based in Silicon Valley. The team focuses on applying cutting-edge Machine Learning technology in products that will shape the future of Mercedes-Benz vehicles with the goal of enhancing our customer’s experience.
In this position, you will work independently and collaboratively with our expert teams to translate state-of-the-art research into scalable, real-world solutions, towards a progressive future in the automotive industry. This position offers a unique opportunity to work at the intersection of ML research and practical engineering, addressing challenges in model optimization, inference speed, and integration within automotive systems.
\n- Investigate and develop advanced techniques for small LLMs, including transformer architectures and synthetic data generation for robust training.
- Explore methods for LLM fine-tuning and optimization, ensuring models are both high-performing and efficient.
- Collaborate with cross-functional teams to integrate LLM solutions into hardware platforms.
- Implement optimization techniques such as quantization, runtime adjustments, and inference speed improvements.
- Work with runtime deployment tools such as ONNX and TensorFlow Lite to optimize model performance on target hardware.
- Develop and evaluate retrieval-augmented generation (RAG) strategies to enhance model performance in dynamic, unstructured data scenarios.
- Document experimental findings, contribute to internal technical reports, and support potential publication efforts in top-tier conferences.
- Participate in team discussions, code reviews, and agile development cycles to continually refine and improve deployment strategies.
- Currently enrolled in MS/PhD program in CS, EE, Math, or a related field, with a strong focus on machine learning, deep learning, and natural language processing
- Proficiency in Python coding, shell scripting, and working within Linux environments
- Demonstrated experience in developing and training deep learning models, especially with transformer architectures and language models
- Extensive experience with deep learning frameworks such a PyTorch and Tensorflow
- Experience with runtime deployment and optimization tools, e.g. ONNX, TensorFlow Lite
- Basic understanding of hardware deployment challenges, including containerization tools like Docker
- Experience with cloud-based tools and platforms such as Azure, Databricks, and Apache Spark
- Knowledge of model optimization techniques such as quantization, inference optimization, and runtime performance enhancements
- Basic knowledge of MLOps practices, including experiment tracking and model versioning using tools such as MLflow
- Understanding of ML workflow: preparing the data, implementing and training ML models, evaluating results, deploying inference on different platforms
- Experience with git or other version control systems
- Experience with synthetic data generation for ML applications
- Prior exposure to LLM fine-tuning and evaluation methodologies
- Hands-on experience with retrieval-augmented generation (RAG) systems
- Familiarity with processing unstructured data in real-world environments
- A record of publications or contributions to reputable AI/ML, CV, or NLP conferences and journals
- Curious, self-motivated, and excited about solving open-ended challenges at Mercedes-Benz
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 (Undergraduate Students)/$32 (Graduate Students)
Salary : $28 - $32
