ML Engineer

Role Overview:

Must-Have Skills & Experience:

• Deep Expertise in Search & Retrieval:

   Proven experience designing, building, and scaling complex, multi-stage search and retrieval systems from the ground up.

• Advanced Indexing & Knowledge Representation:

   Strong background in developing and implementing novel indexing techniques (e.g., vector, graph-based, hybrid) and knowledge representation methods (e.g., knowledge graphs, semantic networks).

• Large-Scale Systems Design:

   Demonstrable experience in architecting and scaling systems to handle massive datasets and high-throughput query loads.

• Proficiency in Relevant Technologies:

   Expertise in Python and hands-on experience with search libraries/frameworks (like Lucene, Faiss, ScaNN) and data processing tools (like Spark, Beam).

• Strong Foundational Knowledge:

   Solid understanding of information retrieval theory, machine learning principles, and data structures.

Good-to-Have Skills & Experience:

• Familiarity with modern LLM architectures and their application in retrieval.
• Experience with real-time data ingestion and processing pipelines.
• Knowledge of graph databases and their application in search.
• Contributions to open-source projects in the search and retrieval space.

What Gets Us Immediately Excited:

• Published Research:

   Top-tier publications (e.g., SIGIR, WSDM, KDD, NeurIPS, ICML) in information retrieval, search, or related fields.

• Novel System Architectures:

   A portfolio demonstrating the design of innovative search or retrieval systems that solved a unique, large-scale problem.

• Patents or Open-Source Leadership:

   Patents filed for novel search/retrieval technologies or significant contributions/leadership in a major open-source project.