Software Engineer Intern - Data
- O ptimize 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
- Optimize Search Infrastructure: Analyze and refactor existing image and video search indexing pipelines to maximize computational efficiency and minimize operational costs.
- Build Iterative Search Features: Design and implement a feedback-loop mechanism that captures positive and negative signals from previous search rounds to dynamically refine and improve search accuracy.
- Develop Smart Sampling Strategies: Overhaul the indexing sampling strategy to intelligently select data based on contextual factors such as predefined critical events, geographic location, and surrounding environmental conditions.
- Cross-Domain Collaboration: Bridge the gap between machine learning models and backend infrastructure to ensure search systems are both highly accurate and horizontally scalable.
- Machine Learning Fundamentals: Solid understanding of ML concepts, particularly in areas related to search relevance, ranking algorithms, or information retrieval.
- Infrastructure & Backend Development: Strong programming proficiency (e.g., Python, C++, Go, or Java) with a focus on building, scaling, and maintaining robust backend infrastructure.
- Algorithm Optimization: Demonstrated ability to analyze code, system architecture, and data pipelines to improve execution speed and resource efficiency.
- Working with Large Datasets: Experience handling and processing high volumes of data, particularly unstructured formats like image and video files.
- Computer Vision Experience: Familiarity with image and video processing techniques, feature extraction, or working with vector embeddings.
- Active Learning & Relevance Feedback: Prior experience building human-in-the-loop systems, reinforcement learning, or systems that actively use user feedback to train or tune models.
- Search & Indexing Tools: Hands-on experience with vector databases (e.g., FAISS, Milvus) or large-scale search engines (e.g., Elasticsearch, Solr).
- Contextual Data Integration: Experience working with geospatial data (GIS), temporal event logging, or incorporating external metadata (like environmental factors) into datasets.
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