Software Engineer Intern - Data

Salary Range: 19 to 65 (Currency: USD) (Pay period: per-hour-wage)

Responsibilities:

• 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.

Required Skills:

• 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.

Preferred Skills:

• 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.