The opportunity
We are seeking an Applied Data Scientist to join our Algorithm team — a hands-on builder at the intersection of LLM systems, agentic AI, and crypto-native intelligence.
What you'll do
Design and implement production-grade LLM pipelines powering Binance AI: Products and next-generation agentic trading features — including multi-step reasoning agents, tool-selection frameworks, and autonomous workflow execution across spot, perpetual, and on-chain markets
Continuously improve agent capabilities in understanding, reasoning, tool: selection, and action execution — optimizing simultaneously for intelligence, latency, and reliability under high-frequency trading constraints
Build and maintain evaluation frameworks for reasoning model outputs in: crypto contexts — covering market analysis accuracy, agent decision quality, hallucination detection, and adversarial robustness against prompt injection in financial workflows
Apply test-time scaling techniques: chain-of-thought, self-consistency, process reward models — to push agent reasoning quality in ambiguous, fast-moving market conditions
Architect AI system components with rigorous attention to inference latency,: throughput, and cost efficiency — leveraging serving frameworks such as vLLM and TensorRT-LLM — in a zero-downtime, 24/7 trading environment
Integrate on-chain data sources, wallet intelligence, and crypto market: signals into LLM-powered analytical pipelines — building the data layer that makes Binance's agents genuinely crypto-native
What they're looking for
- Bachelor's or Master's degree in Computer Science, Electrical Engineering,: Statistics, Mathematics, or related technical field
- –5 years of industry or research experience in applied ML or AI engineering
- Strong Python programming skills ; Equally important: demonstrated comfort with vibe coding — using AI-assisted development tools fluidly as core part of your workflow
- Demonstrated hands-on experience with LLMs: prompt engineering, post-training, or end-to-end LLM application development
- Familiarity with multi-agent system design: task decomposition, tool use via MCP, memory management, parallel agent execution, and inter-agent communication
- Strong analytical thinking and problem decomposition; comfortable operating: under ambiguity in fast-moving environments
