The opportunity
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.
What you'll do
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
What they're looking for
- 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
