| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
Binance | Taipei, Taiwan |
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Integra | Remote | $21k - $64k | |||
Integra | Remote | $88k - $101k | |||
Integra | Remote | $72k - $84k | |||
| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
molecule.xyz | Remote | $94k - $150k | |||
Shakepay | Remote | $200k - $250k | |||
Shakepay | Remote | $150k - $200k | |||
Binance | Hong Kong, Hong Kong |
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Binance | Taipei, Taiwan |
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Binance | Taipei, Taiwan |
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Binance | Hong Kong, Hong Kong |
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Binance | Taipei, Taiwan |
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Phantom | Remote | $185k - $225k | |||
Mysten Labs | United States | $164k - $225k | |||
Binance | Hong Kong, Hong Kong |
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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 â 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 â 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.Â
Responsibilities
- Design and run experiments in reasoning model training, post-training alignment, test-time compute scaling, and systematic model evaluation â 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 â 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 â 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 â 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 bonusÂ
- Comfortable using AI-assisted development tools as a natural part of your research workflow â 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