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.
Job Responsibilities:
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.
Minimum Qualifications:
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
Preferred Qualifications:
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)
