The opportunity
For all students looking to gain hands-on experience! You'll work directly with senior scientists on research problems at the frontier of LLM reasoning, post-training methodology, and agentic AI — applied to global crypto markets.
What you'll do
Contribute to the design and execution of experiments in reasoning model: training, post-training alignment, test-time scaling, or systematic model evaluation — with a focus on applications in financial and crypto-native contexts
Review and synthesize recent academic literature at NeurIPS, ICML, ICLR, and: ACL — tracking developments in reasoning, alignment, and agentic AI to inform and sharpen ongoing research directions
Implement model variants, training procedures including RLVR-based: approaches, and evaluation protocols using PyTorch and the Hugging Face ecosystem
Track and log experiments systematically using Weights & Biases or equivalent: — maintaining reproducibility standards throughout
Explore the intersection of LLM reasoning and crypto-native data: on-chain signals, market microstructure, multi-modal market intelligence — identifying research opportunities unique to Binance's position
Collaborate with applied engineering teams to understand how research: findings translate into production constraints in a zero-downtime, 24/7 trading environment
What they're looking for
- Currently pursuing a Master's or PhD in Machine Learning, Computer Science,: Mathematics, or related field strongly preferred;
- Expected graduation in 2026, 2027, or 2028
- Strong Python programming skills and PyTorch proficiency; C++ or Rust: exposure a plus. Equally important: demonstrated comfort with vibe coding — using AI-assisted development tools fluidly as part of your research and experimentation workflow
- Solid understanding of transformer architectures, large language model: pretraining, and the evolution toward reasoning models
