Prediction Markets Trader
Quick Summary
Monitor and trade active markets in real time across Polymarket (CLOB/Gamma/Subgraph APIs) and Kalshi (FIX/WebSocket/REST). Execute strategies including: market making with dynamic skew,
Bachelor's or higher in Quantitative Finance, Statistics, Computer Science, Economics, Mathematics, or equivalent. Strong programming skills: Python (pandas, NumPy, backtesting).
Responsibilities
~1 min read- →Monitor and trade active markets in real time across Polymarket (CLOB/Gamma/Subgraph APIs) and Kalshi (FIX/WebSocket/REST).
- →Execute strategies including: market making with dynamic skew, microstructure arbitrage (order-flow and book-imbalance sniping), cross-platform arb, news/event momentum (sub-second reaction), and statistical pairs/mean-reversion.
- →Develop and backtest models using historical tick data, Bayesian probability updates, NLP sentiment parsing, and ML for fair-value estimation.
- →Collaborate with engineering on low-latency infrastructure (API integrations, order engines, anomaly detection).
Requirements
~1 min read- Bachelor's or higher in Quantitative Finance, Statistics, Computer Science, Economics, Mathematics, or equivalent.
- Strong programming skills: Python (pandas, NumPy, backtesting).
- Solid foundation in probability, statistics, time-series analysis, and Bayesian methods.
- Hands-on experience with prediction market platforms (Polymarket and/or Kalshi APIs) or closely related domains (options, binary events, sports/event betting).
- Genuine, demonstrated interest in prediction markets — we expect you to be actively engaged with the space (personal trading, deep reading of protocols, following major resolutions, contributing to discussions, etc.). This is non-negotiable.
- Prior profitable trading experience in prediction markets, options, or event-driven strategies (personal or professional).
- Familiarity with blockchain/DeFi tools (ethers.js, Polygon RPC, USDC wallets).
- Experience with low-latency systems, FIX protocol, or Web3 integrations.
- Track record of building and backtesting quantitative models with real historical data.
- Deep domain knowledge of high-impact events (politics, macroeconomics, climate, sports).
- Resume
- Cover letter explicitly detailing your genuine interest in prediction markets (specific examples of your engagement: trades you’ve made, resolutions you’ve studied, models you’ve built, protocols you follow, etc.)
- GitHub, personal blog, or portfolio links showing relevant work
Applications reviewed on a rolling basis. Strong candidates will receive a technical screen, strategy discussion, and live coding/modeling exercise focused on real prediction market scenarios.
Requirements
~1 min readListing Details
- Posted
- February 27, 2026
- First seen
- March 26, 2026
- Last seen
- April 21, 2026
Posting Health
- Days active
- 25
- Repost count
- 0
- Trust Level
- 34%
- Scored at
- April 21, 2026
Signal breakdown
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