Real-world Feedback Loops: Leverage multi-channel user feedback and real-world task data as primary research signals; design experiments and datasets to continuously improve agent and retrieval performance in production scenarios
1+ Year hands-on experience with LLM, RAG and AI agent systems in production
RAG & Agentic RAG Engineering: Hands-on experience building production retrieval pipelines end-to-end — embedding models (BGE, OpenAI, etc.), vector stores (Qdrant, Milvus, Pinecone, Weaviate), hybrid search (keyword + vector), reranking models; deep understanding of chunking strategy, text cleaning, and multimodal data parsing; experience implementing Agentic RAG patterns — Self-RAG, Corrective RAG, adaptive retrieval, multi-hop decomposition, retrieve-reflect-refine loops
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Perform ad-hoc reviews of trading activity by analyzing complex situations, identifying high risk behaviors, and applying judgment to resolve issues in an efficient manner.
Compile analysis into meaningful summaries and make findings available to relevant stakeholders such as Legal and Compliance.
Keep informed of regulatory, business, and market structure changes that impact the surveillance program and continually look for methods to improve coverage and performance.
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Prepare and maintain Business Requirement Documents (BRDs), compliance trackers, meeting summaries, and status reports for ongoing regulatory requirements, product suggestions & workstreams.
Coordinate and align on a regular basis with cross-functional stakeholders (Legal, Product, Engineering, local compliance teams, and regional leads) as well as internal stakeholders within Binance Earn.
Monitor regulatory developments and product configuration changes across markets, flagging risks and open issues to the team proactively.
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Content Ecosystem Analysis: Evaluate the performance of different content formats, topics, and creators, providing data-driven support for content operations, creator incentive programs, and community campaigns.
Strategy and Impact Evaluation: Support data preparation and performance analysis for product features, operational campaigns, and A/B tests. Help summarize findings and recommend improvements.
Data Support and Quality Assurance: Extract and analyze data based on the needs of product and operations teams. Participate in event-tracking validation, metric definition and documentation, and data quality checks.
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