Quant/ML Researcher at hirix

hirix Quant/ML Researcher Work format: remote is possible

☑️ Responsibilities

Full cycle of research on short-term microstructure, LP/hedging, execution, inter-venue signals, risk distribution, and related crypto/DeFi tasks
Research design: hypothesis, target/labeling, baseline, metrics, causally correct OOS testing, regime analysis, and explicit GO/NO-GO criteria
Data handling: source identification, time alignment, quality control, feature engineering, and reproducible data contracts
ML/DL based on the task structure; RL or other sequential control methods – only when truly justified
Realistic simulation and trading economics: fees, slippage, liquidity, latency, adverse selection, impact, fill assumptions, hedging, and DEX/CEX synchronization
Paper/shadow strategies, sim-to-real analysis, and collaboration with quant developers in transferring proven solutions to production
Research engineering standards: reproducible experiments, clean Python, critical checks, monitoring, and disciplined use of LLM/agents

☑️ Candidate Requirements

Verifiable experience of 2-3 Quant/ML projects where you were personally responsible for a significant part of the chain from problem definition and data to testing, code, and the decision to implement or GO/NO-GO
Excellent Python and strong engineering foundation: you write, debug, and review research code yourself
Strong statistics, probability, optimization, and time series analysis skills: temporal dependence, leakage, selection bias, uncertainty, and honest OOS testing
Deep practical understanding of microstructure and execution: order book/flow, liquidity, latency, fees, slippage, adverse selection, impact, sizing, and hedging
Strong practical proficiency in ML/DL for sequential financial data and the ability to distinguish real signal from metric optimization
High autonomy: structure unclear problems, choose the most informative experiment, quickly stop weak hypotheses, and make evidence-based decisions
Working understanding of the crypto market structure. Direct DeFi/DEX experience is a plus, but strong CEX/microstructure experience can compensate with rapid adaptation

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