Pioneer Talent Program - Applied Data Scientist

Responsibilities

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
Partner with research scientists to translate experimental findings into production-grade agentic solutions with clear performance benchmarks

Qualifications

Bachelor's or Master's degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or related technical field
0–5 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