Machine Learning Engineer Intern

Team: Data

Location: Santa Clara, CA

Commitment: Intern

Workplace Type: onsite

Salary:

Responsibilities:

• Build an AI Assistant: Develop and deploy an internal AI chatbot that allows employees to query company knowledge and test results using natural language.
• Implement RAG Architecture: Design and build a secure Retrieval-Augmented Generation (RAG) pipeline to pull contextual data from internal sources without compromising data privacy.
• Develop Data Pipelines: Create automated pipelines to ingest, clean, and structure data from diverse sources, including internal documents, Slack conversations, and autonomous driving databases (bagdb, pluscene, and right-seater logs).
• Fine-Tune Open-Source LLMs: Work with open-source models (such as Qwen) and fine-tune them to accurately understand and process company-specific terminology and AV testing metrics.
• Generate Actionable Insights: Enable the system to synthesize complex data across simulation and road tests to answer questions about passing rates, test mileages, coverage gaps, and testing recommendations.

Required Skills:

• Machine Learning & NLP: Solid understanding of Large Language Models (LLMs), natural language processing, and prompt engineering.
• Python Programming: Strong proficiency in Python for machine learning workflows, scripting, and backend system integration.
• Data Engineering Fundamentals: Experience building data extraction, transformation, and loading (ETL) pipelines, as well as handling both structured and unstructured data.
• Familiarity with RAG: Core understanding of Retrieval-Augmented Generation workflows, text chunking, and vector embeddings.

Preferred Skills:

• Open-Source LLM Experience: Hands-on experience deploying, fine-tuning, or quantizing open-source models (e.g., Qwen, LLaMA, Mistral) using frameworks like Hugging Face or vLLM.
• Vector & Relational Databases: Experience working with vector databases (e.g., Milvus, Chroma, FAISS) as well as querying traditional SQL/NoSQL databases.
• Autonomous Vehicle Domain Knowledge: Familiarity with autonomous driving data formats (e.g., ROS bags), simulation environments, or road testing metrics.
• Chatbot Frameworks: Experience with LLM orchestration frameworks such as LangChain or LlamaIndex.
• Data Security & Privacy: An understanding of best practices for deploying ML models locally or within secure, internally-hosted environments.