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Join hirix as a Quant/ML Researcher to conduct comprehensive research in crypto/DeFi microstructure, focusing on data analysis, ML/DL methods, and realistic trading simulations.
Join hirix as a Quant/ML Researcher to conduct comprehensive research in crypto/DeFi microstructure, focusing on data analysis, ML/DL methods, and realistic trading simulations.
Responsibilities
- Full cycle research on short-term microstructure, LP/hedging, execution, inter-exchange signals, risk distribution, and related crypto/DeFi tasks.
- Research design: hypothesis, target/markup, baseline, metrics, causally correct OOS verification, regime analysis, and explicit GO/NO-GO criteria.
- Data work: source finding, time alignment, quality control, feature construction, and reproducible data contracts.
- ML/DL according to task structure; RL or other sequential control methods only when truly justified.
- Realistic simulation and trading economics: commissions, slippage, liquidity, delays, adverse selection, impact, fill assumptions, hedging, and DEX/CEX synchronization.
- Paper/shadow strategies, sim-to-real analysis, and collaboration with quant developer to transfer proven solutions to production.
- Research engineering standard: reproducible experiments, clean Python, critical checks, monitoring, and disciplined use of LLM/agents.
Requirements
- Proven experience in 2-3 Quant/ML projects where you were personally responsible for a significant part of the chain from task setting and data to verification, code, and implementation decision or GO/NO-GO.
- Excellent Python and strong engineering background: you write, debug, and review research code yourself.
- Strong statistics, probability, optimization, and time series work: temporal dependence, leakage, selection bias, uncertainty, and fair OOS verification.
- Deep practical understanding of microstructure and execution: order book/flow, liquidity, latency, fees, slippage, adverse selection, impact, sizing, and hedging.
- Strong practical knowledge of ML/DL for sequential financial data and ability to distinguish real signals from metric optimization.
- High independence: structure unclear problems, choose the most informative experiment, quickly stop weak hypotheses, and make decisions based on evidence.
- Working understanding of crypto market structure. Direct experience in DeFi/DEX is a plus, but strong CEX/microstructure experience can compensate with quick adaptation.