Search Systems Engineer - Remote

Overview

This role involves designing indexing, ranking, and evaluation pipelines that combine traditional IR methods with neural re-rankers. You’ll build scalable, low-latency search systems capable of serving massive text corpora with high relevance and reliability.

In Short

• Design and implement hybrid IR systems that blend traditional retrieval methods with neural re-rankers.
• Build and optimize indexing and ranking pipelines using Elasticsearch/OpenSearch and FAISS.
• Develop neural re-rankers with Hugging Face Transformers and PyTorch.
• Combine BM25, TF-IDF, and neural models into hybrid search frameworks.
• Design and implement evaluation pipelines to benchmark retrieval performance.
• Optimize systems for low-latency performance at scale.
• Collaborate with researchers and engineers to integrate IR pipelines into production AI systems.

Requirements

• Background in computer science, information retrieval, or data engineering.
• Proficient with Elasticsearch/OpenSearch for indexing and querying large text stores.
• Experience with neural search and reranking using FAISS, Hugging Face, and PyTorch.
• Understanding of traditional IR methods such as BM25 and TF-IDF and how to integrate them with modern ML approaches.
• Ability to design low-latency pipelines that serve results efficiently at scale.
• Comfortable working with pandas for data analysis and building evaluation workflows.
• Experience integrating pipelines into systems via REST APIs.

Benefits

• Part-time (20–30 hrs/week) with flexible hours—work from anywhere, on your schedule.
• Weekly Bonus of $500–$1000 USD per 5 tasks.
• Remote and flexible working style.