We’re building a lean, high-performance engineering team to develop market-making and HFT systems on DEXs and orderbook aggregators, along with ML-driven predictive signals and venture token infrastructure. We’re seeking a highly skilled engineer / quant developer to help design and build our core trading engine, with the opportunity to: Shape alpha-generating MM strategies in DeFi and on-chain orderbooks Integrate ML models for whale flows, social sentiment, and market microstructure Build modular, production-ready trading infrastructure Work on venture token allocations, LP programs, and emerging DeFi opportunities This is a contractor role, flexible, and designed to scale as we grow. Your engineering skill here can directly drive the system’s performance and returns. -- Screening Questions per Competency (for discussion): A. Market-Making / HFT Strategy Design Describe a market-making strategy you built or optimized. How did you choose spreads and sizes? How do you adjust quoting logic in volatile conditions? Have you implemented skewing based on aggressor flow or inventory? Can you explain an MM improvement you made that increased realized PnL significantly? B. Exchange / DEX API Integration Which CEX / DEX APIs have you integrated with? REST vs WebSocket? How do you handle failed orders, latency, partial fills, or rate limits? Have you worked on orderbook aggregation across multiple venues? Can you show sample code (or pseudocode) for syncing orderbooks and executing orders? C. Low-Latency Execution & System Design Have you built systems for sub-second order execution? How did you minimize bottlenecks in data ingestion and trade execution? What design choices impact latency most in an HFT/MM engine? How do you monitor and troubleshoot real-time performance? D. ML / Data Signal Integration Have you implemented ML models for time-series or event-driven trading signals? How would you ingest whale wallets or social chatter data for short-term alpha? Explain a feature engineering approach you used for predictive trading models. How do you validate or backtest ML signals in live environments? E. Capital & Risk Management How do you enforce inventory limits, max exposure, or kill-switch rules in a bot? Explain a scenario where your risk measures prevented a loss. How would you adjust risk limits dynamically during volatile market conditions? F. Engineering / Coding Quality Can you walk me through code architecture for your last MM/ML project? How do you structure modular bots to allow incremental feature addition? What tools or practices do you use for testing, logging, and monitoring?