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

Job description

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. Binance is trusted by more than 320 million people in 100+ countries for its industry-leading security, transparency, trading engine speed, protections for investors, and unmatched portfolio of digital asset products and offerings from trading and finance to education, research, social good, payments, institutional services, and Web3 features. Binance is devoted to building an inclusive crypto ecosystem to increase the freedom of money and financial access for people around the world with crypto as the fundamental means. **About Binance Accelerator Program** Binance Accelerator Program (BAP) is a 3-6 month internship program designed for Early Career talent to have firsthand experience in the rapidly expanding digital assets space. You will be given the opportunity to develop your skills at Binance and understand what it’s like to work at the world's leading blockchain ecosystem. As part of your internship in the BAP, there will also be opportunities for networking and development, which will expand your professional network and build transferable skills to propel you forward in your career. Learn about the BAP Program [HERE](https://www.binance.com/en/blog/all/early-career-program-how-binance-gears-up-early-career-talent-6771140985933048651). **Who may apply** Current university students and recent graduates. *\*Terms of employment / engagement shall be subject to contract and local applicable laws* **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