Senior Financial AI Engineer (Data & Knowledge Engineering) at Binance

Role Overview

As a Senior Financial AI Engineer at Binance, you will be instrumental in developing and operating production-grade AI data processing and knowledge services specifically tailored for Binance’s equities business. Your primary responsibility will be to transform diverse financial information, including announcements, news, earnings reports, earnings call transcripts and audio/video, and research reports, into actionable data, knowledge, and evidence. This structured information will directly feed into trading products and Binance’s AI systems. Core scenarios for this role involve knowledge engineering, maintaining knowledge bases, and implementing Retrieval-Augmented Generation (RAG) systems. Furthermore, you will be tasked with building reusable processing frameworks and overseeing model and rule integration, task orchestration, servitization, quality control, cost management, and ensuring production stability.

Responsibilities

• Own the development and operation of production-grade AI data processing pipelines for various financial content, including text, tables, layouts, and audio/video. This covers critical tasks such as chunking, deduplication, clustering, standardization, versioning, and validation of results.
• Construct layered financial knowledge bases that effectively manage source documents, structured facts, entities and events, full-text and vector indices, and relationship data. You will also maintain essential metadata, including source, time, security, market, language, version, and authorization.
• Engineer and operate RAG services in a production environment. This involves building query processing, permission and time filtering, multi-route retrieval, reranking integration, context assembly, evidence citation, and result return pipelines, ensuring full traceability to original sources.
• Design extensible frameworks for AI data processing, knowledge engineering, and indexing. These frameworks will feature unified interfaces to accommodate new data sources, formats, languages, models, rules, and algorithms, supporting incremental updates, index rebuilds, historical backfill, deletion, and authorization expiry.
• Integrate Large Language Models (LLMs), document understanding models, Natural Language Processing (NLP) models, rule systems, and other algorithm components into cohesive pipelines with clear input/output contracts, task orchestration, version governance, and robust failure handling.
• Take ownership of engineering capabilities for AI data processing and knowledge services, including APIs, asynchronous tasks, queues, caching, retry mechanisms, graceful degradation, human review processes, canary releases, rollbacks, fault recovery, and capacity governance.
• Establish a comprehensive quality system for AI data processing, knowledge, and RAG. This includes measuring parsing accuracy, retrieval coverage, citation completeness, staleness, latency, stability, and cost, alongside performing tiered root-cause analysis.
• Collaborate effectively with data, algorithm, product, and compliance teams to ensure the reliable deployment of AI data processing and knowledge services in user-facing equities products and Binance AI.

Requirements

• Master’s degree or higher in Computer Science, Software Engineering, AI, or a closely related field.
• Minimum of 5 years of professional experience in backend development, data platforms, Machine Learning (ML) engineering, or AI application engineering.
• Familiarity with equity markets and the investor research and decision-making workflow. This includes an understanding of trading mechanics, market data, fundamentals, corporate actions, and major market events, as well as the ability to assess entities, time sensitivity, sources, and usage boundaries of financial information.
• Proficiency in Python and at least one other backend language such as Java. Strong skills in software engineering, distributed systems, and service interface design are essential.
• Production experience with LLMs, NLP, or ML systems, demonstrating the ability to explain model invocation, task orchestration, failure recovery, version governance, and online issue resolution.
• Production experience with knowledge engineering or RAG systems, including familiarity with structured, semi-structured, and unstructured content processing. You should be able to articulate the complete pipeline from source ingestion through retrieval, citation, and online feedback.
• Familiarity with full-text search, vector search, document storage, and their combinations. Understanding of concepts such as chunking, indexing, filtering, recall, reranking, context assembly, citation, and permission control, without being tied to any specific database or framework.
• Experience with performance, stability, cost, and observability governance for high-concurrency or large-scale processing systems, extending beyond model API calls or demo prototypes.
• Demonstrated experience in adapting new data sources or content types, with the ability to distill source-specific logic into reusable processing capabilities.

Why Binance

• Shape the future within the world’s leading blockchain ecosystem.
• Collaborate with world-class talent in a user-centric global organization with a flat structure.
• Tackle unique, fast-paced projects with autonomy in an innovative environment.
• Thrive in a results-driven workplace with opportunities for career growth and continuous learning.
• Enjoy a competitive salary and comprehensive company benefits.
• Benefit from a work-from-home arrangement (which may vary based on the specific business team’s work nature).

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