Binance Accelerator Program - AI Research Scientist (LLM Reasoning & Post-Training)
at Binance
Posted 16 hours ago
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**Binance AI Research Scientist - Large Language Models (LLM) Reasoning & Post-Training** drives cutting-edge AI innovation in Binance's blockchain ecosystem. This early-career role, part of the Binance Accelerator Program, requires expertise in LLM reasoning and post-training for crypto-centric AI applications. Key responsibilities involve developing novel AI models, enhancing research skills, and collaborating with cross-functional teams. Proficiency in Python, PyTorch/TensorFlow, and experience with LLM frameworks like BLOOM or LLaMA are essential. This 3-6 month internship offers professional development and networking opportunities within the world's leading blockchain ecosystem.
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
