Binance Accelerator Program - AI Research Scientist (LLM Reasoning & Post-Training)

About the Role

You'll work alongside senior research scientists on problems at the frontier of LLM reasoning, post-training methodology, and agentic AI - in one of the few environments where your models interact with live global markets at scale.

This isn't a support or literature-review role. You'll run experiments, form independent hypotheses, implement ideas from recent papers, and work closely with engineering teams to understand how research behaves under real production constraints - 24/7, zero-downtime, hundreds of millions of users.

Who may apply

Current university students (Masters, PHD in AI track) or recent graduates who don't mind starting as intern.

Responsibilities

• Design and run experiments in reasoning model training, post-training alignment, test-time compute scaling, and systematic model evaluation - grounded in financial and crypto-native problem settings
• Implement model variants, training pipelines (including RLVR-based approaches), and evaluation frameworks in PyTorch and the Hugging Face ecosystem
• Synthesize recent work from NeurIPS, ICML, ICLR, and ACL to sharpen active research directions - not just track the field, but translate it into testable ideas
• Apply LLM reasoning to crypto-native data: on-chain signals, market microstructure, and multi-modal market intelligence - research opportunities that don't exist anywhere else
• Maintain rigorous experiment tracking and reproducibility standards (W&B or equivalent)
• Partner with applied engineering to understand how research translates into production systems - and what constraints actually matter

Requirements

• Currently pursuing a Master's or PhD in Machine Learning, Computer Science, Mathematics, or a related field (preferably graduating between 2026 to 2028)
• Strong Python and PyTorch fundamentals; C++ or Rust exposure is a bonus
• Comfortable using AI-assisted development tools as a natural part of your research workflow - not as a crutch, but as leverage
• Solid grounding in transformer architectures, LLM pretraining, and the shift toward reasoning-capable models
• You form opinions about research, not just summaries of it