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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.