The posting, in Kalshi's own words
archived Sep 28, 2026What is Kalshi?
Kalshi has defined a new category: prediction markets. Kalshi allows people to trade on the outcome of any events and turn any question about the future into a financial asset. Kalshi fought for years and legalized prediction markets in the US for the first time in history, is currently the fastest growing financial market in America, and has thousands of markets across politics, economics, financials, weather, tech, AI, culture and more. We believe prediction markets have the potential to be the largest financial market because they turn anything into a financial position. Our vision: well… build the largest financial market on the planet. Our mission: bring more truth to the world through the power of markets. Our culture is simple: we hire really talented people, work really hard, and enjoy the climb. We are looking for ambitious and exceptional people to join our (relatively small) team to help us build the next generation of financial markets.
Role Roadmap
We are looking for someone to own the operations and infrastructure behind Kalshi’s weather markets. You will turn weather data into clear contracts, reliable markets, and accurate settlements, while building the automation that allows the category to scale. This is a hands-on role. You will write code, investigate station data, manage data partners, and make sound settlement decisions under time pressure. When a report is revised or a station goes down, you will know how to investigate and apply the contract terms.
Read the full posting ↓
What You’ll Do
Own settlement, monitor official reports, investigate discrepancies, and resolve settlement issues accurately and consistently with contract terms and exchange rules. Design and launch contracts. Write clear terms for temperature, rain, snow, and hurricane markets. Anticipate trace precipitation, missing observations, report revisions, station relocations, and time-boundary issues before launch. Manage Synoptic and other data relationships, including service levels, quality checks, and fallback sources permitted by contract terms. Understand when vendor observations diverge from the official settlement source. Build and maintain Python and SQL workflows that list, monitor, and settle hundreds of city-day markets, with reliable orchestration, alerting, and exception handling. Partner with legal and compliance on contract self-certification, settlement procedures, and void policies. Maintain clear, reproducible audit trails for settlement decisions. Work with trading, risk, product, and engineering to identify pricing and liquidity issues and expand weather markets for retail traders and institutional hedgers.
What You Bring
Practical knowledge of National Weather Service Climatological Reports (CLI) and Automated Surface Observing System (ASOS) station behavior, including outages, revisions, preliminary versus final values, and local standard time versus daylight time. Experience running operational systems or data workflows where accuracy and reliability matter. You can investigate an ambiguous issue, make a defensible decision, and own the outcome. Proficiency in Python and SQL, with experience building and operating automated data pipelines using Dagster or a similar orchestration framework. Experience managing providers, service-level expectations, data quality, and fallback procedures. You can distinguish live observations from the authoritative source specified in a contract. The ability to translate complex weather events and imperfect data into precise, unambiguous settlement terms. Experience working under exchange rules, or the ability to quickly learn Part 40 self-certification, void policies, and settlement documentation requirements. You are comfortable handling time-sensitive issues, including early-morning settlement questions, and building systems that prevent recurring problems.
Bonus Points
You understand how market makers price weather and can recognize when liquidity incentives or trading activity create directional weather exposure. Familiarity with utilities, energy traders, agriculture, reinsurance, or other institutional users of weather markets. An understanding of ensembles and model biases, and how they inform market design and surveillance.