| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
Binance | Taipei, Taiwan |
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Injective | New York, NY, United States | $105k - $120k | |||
SBI Investment | Remote | $80k - $110k | |||
Tether | Remote |
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| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
Fireblocks | United States | $202k - $265k | |||
HYR Global Source Inc | Chicago, IL, United States | $80k - $85k | |||
Delta System & Software, Inc. | Chicago, IL, United States | $87k - $144k | |||
UNICOM Technologies Inc | Chicago, IL, United States | $80k - $85k | |||
FUSTIS LLC | Chicago, IL, United States | $153k | |||
Accord Technologies Inc | Chicago, IL, United States | $124k - $134k | |||
Hivemind Capital Partners | New York, NY, United States | $125k - $160k | |||
Binance | Taipei, Taiwan |
| |||
Anza | United States | $140k - $150k | |||
Ondo Finance | United States | $98k - $150k | |||
Tonkeeper | Remote | $91k - $115k |
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