As an ML Infrastructure & data engineer Intern on our team, you’ll play a critical role in building and optimizing our data engine platform, which is the backbone of our autonomous driving technologies. Your contributions will directly impact the reliability, scalability, and performance of our AI systems by designing robust ML infrastructure, refining data pipelines, and enabling efficient workflows.
Our internship hourly rates are a standard pay determined based on the position and your location, year in school, degree, and experience.
Responsibilities:
Design and implement LLM/VLM-driven autolabeling systems to optimize data annotation workflows and reduce manual effort.Architect and implement scalable training data pipelines for diverse deep learning models (eg., perception, planning).Develop version-controlled datasets with efficient storage and retrieval mechanisms to ensure data integrity and accessibility.Create visualization and analysis tools to monitor model performance and dataset quality.Develop automation for continuous integration and deployment of updated models, ensuring streamlined updates and reliability.,
Required Skills:
Pursuing M.S or PhD in Computer Science, Mathematics, AI/ML, or related field.1+ years of hands-on experience in machine learning (e.g., deep learning, NLP) or infrastructure engineering.Proficiency in Python, with strong experience in pandas, NumPy, and SQL (writing efficient queries).,
Preferred Skills:
Experience with ML frameworks (e.g., PyTorch) and infrastructure tools (CI/CD, cloud platforms).Knowledge of data engineering best practices (e.g., ETL, data versioning, metadata management) and containerization tools (Docker, Kubernetes).