PlusAI

Physical AI

MachineLearningEngineerIntern

$0–0k Santa Clara, California, United States INTERNSHIP Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Entry candidates.

The Brief

“Machine Learning Engineer Intern at PlusAI. Skills: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Python, Data Engineering, Simulation Technology. Develop and deploy an internal AI chatbot that allows employees to query company knowledge and test results using natural language. Design and build a secure Retrieval-Augmented Generation (RAG) pipeline to pull contextual data from internal sources without compromising data privacy”

What You'll Achieve.

generate realistic, scalable simulation scenarios from text and real road data; expand our capabilities in testing autonomous vehicles using large-scale simulation with LLM-driven solutions

Industry & Context.

Physical AI
Problems you'll solve

synthesize complex data across simulation and road tests to answer questions about passing rates, test mileages, coverage gaps, and testing recommendations

What They're Looking For.

Must Have

Solid understanding of Large Language Models (LLMs), natural language processing, prompt engineering, proficiency in Python for machine learning workflows, scripting, and backend system integration, Experience building data extraction, transformation, and loading (ETL) pipelines, handling both structured and unstructured data, Core understanding of Retrieval-Augmented Generation workflows, text chunking, vector embeddings

Nice to Have

Hands-on experience deploying, fine-tuning, or quantizing open-source models (e. g. , Qwen, LLaMA, Mistral) using frameworks like Hugging Face or vLLM, Experience working with vector databases (e. g. , Milvus, Chroma, FAISS), querying traditional SQL/NoSQL databases, Familiarity with autonomous driving data formats (e. g. , ROS bags), simulation environments, road testing metrics, Experience with LLM orchestration frameworks such as LangChain or LlamaIndex, An understanding of best practices for deploying ML models locally or within secure, internally-hosted environments

What You'll Do.

Develop and deploy an internal AI chatbot that allows employees to query company knowledge and test results using natural language

Design and build a secure Retrieval-Augmented Generation (RAG) pipeline to pull contextual data from internal sources without compromising data privacy

Create automated pipelines to ingest

and structure data from diverse sources

including internal documents

and autonomous driving databases (bagdb

and right-seater logs)

Work with open-source models (such as Qwen) and fine-tune them to accurately understand and process company-specific terminology and AV testing metrics

Enable the system to synthesize complex data across simulation and road tests to answer questions about passing rates

and testing recommendations

Full Job Description

## Description PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams. We’re seeking an enthusiastic and driven Simulation/ML Engineer Intern to join our team and work on an exciting project that blends Large Language Models (LLMs) with simulation technology. In this role, you’ll help develop a tool that can generate realistic, scalable simulation scenarios from text and real road data. This is a fantastic opportunity to apply your machine learning and robotics knowledge to real-world challenges, while working on a project that will revolutionize how simulation scenarios are created, with minimal manual effort. You’ll be at the forefront of innovation, helping us expand our capabilities in testing autonomous vehicles using large-scale simulation with LLM-driven solutions. ## 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

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