Software Engineer Intern - Data

PlusAI•Santa Clara, CA

15d•$19 - $65

About The Position

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 are seeking a Software Engineer Intern to join our team and contribute to the development of externally shareable metrics dashboards. This project aims to: Optimize existing image/video search indexing to make it really efficient/cost effective to run. Enhance both image/video search with an iterative search feature so we can based on previous rounds of iteration to accurately collect positive/negative signals and greatly improve search results. Improve Image/video search indexing sampling strategy so that it will do a smart sample based on different factors like important predefined events, geo location, surrounding environment, etc. Area of work: Infrastructure, Machine Learning

Requirements

Solid understanding of ML concepts, particularly in areas related to search relevance, ranking algorithms, or information retrieval.
Strong programming proficiency (e.g., Python, C++, Go, or Java) with a focus on building, scaling, and maintaining robust backend infrastructure.
Demonstrated ability to analyze code, system architecture, and data pipelines to improve execution speed and resource efficiency.
Experience handling and processing high volumes of data, particularly unstructured formats like image and video files.

Nice To Haves

Familiarity with image and video processing techniques, feature extraction, or working with vector embeddings.
Prior experience building human-in-the-loop systems, reinforcement learning, or systems that actively use user feedback to train or tune models.
Hands-on experience with vector databases (e.g., FAISS, Milvus) or large-scale search engines (e.g., Elasticsearch, Solr).
Experience working with geospatial data (GIS), temporal event logging, or incorporating external metadata (like environmental factors) into datasets.

Responsibilities

Analyze and refactor existing image and video search indexing pipelines to maximize computational efficiency and minimize operational costs.
Design and implement a feedback-loop mechanism that captures positive and negative signals from previous search rounds to dynamically refine and improve search accuracy.
Overhaul the indexing sampling strategy to intelligently select data based on contextual factors such as predefined critical events, geographic location, and surrounding environmental conditions.
Bridge the gap between machine learning models and backend infrastructure to ensure search systems are both highly accurate and horizontally scalable.