Binance Accelerator Program - AI Research Scientist (LLM Reasoning & Post-Training)
Company: Binance ↗
Location: Taiwan, Taipei
Published:
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
Source: web3.career