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JOB DETAILS

Quant Developer - Systematic Commodities Hedge Fund

CompanyMoreton Capital Partners
LocationCiudad de México
Work ModeOn Site
PostedAugust 29, 2026
About The Company
We extract alpha by combining deep fundamental trading experience with cutting-edge quantitative research. While the machine handles complex pattern recognition with speed and precision, human oversight ensures strategic decision-making and adaptability to shifting market conditions and regime changes, allowing us to capture real-time opportunities and manage risk effectively.
About the Role
Quant Developer – Systematic Commodities Hedge Fund

Moreton Capital Partners is seeking a talented Quant Developer to join our team at the ground floor of an ambitious build. We are launching live trading across global commodity futures, supported by an investment process rooted in machine learning.

This is a unique opportunity to work directly with the global team, owning infrastructure that takes research ideas to production in a fast-moving, real capital environment.

Key Responsibilities
  • Build and maintain data pipelines ingesting futures, options, and alternative datasets (from price data from Bloomberg and vendor feeds to unstructured data).
  • Improve backtesting framework (event-driven simulations, realistic slippage/costs, walk-forward validation, portfolio performance analysis).
  • Support research tooling: feature libraries, experiment tracking, artefact storage.
  • Machine learning cloud and local execution and optimization setup.
  • Productionize signals into the live trading stack with CI/CD, monitoring, and version control.
  • Develop dashboards and alerting for data quality, latency, and model drift.
  • Collaborate with researchers to translate hypotheses into robust, testable experiments, as well as enhance proposed process computationally.
  • Fluency in Python and SQL; clean, testable code is a must.
  • Experience with data engineering (Airflow, Snowflake, pandas, polars workflows).
  • Prior exposure to systematic trading, backtesting, or market data pipelines.
  • Familiarity with cloud environments (AWS), containers (Docker), and CI/CD.
  • Self-starter with the ability to work autonomously in a lean, high-ownership environment.
  • Bachelors in CS/Comp-Eng or computationally heavy subject matter, and ideally, a minor in Finance.

Bonus points for:

  • Commodities or macro markets exposure.
  • Systematic medium term investment exposure.
  • Experience with ML Ops tools (MLflow, Weights & Biases), feature stores, or model monitoring.
  • Front-end skills (TypeScript/React) to help build researcher dashboards.
  • Impact from day one: You’ll be building mission-critical infrastructure for a fund that begins live trading this year for large institutional investors.
  • Direct exposure: Work alongside the CIO and senior researchers, with a direct line to decision-making.
  • Learning curve: Deep exposure to commodity markets, ML research workflows, and institutional-grade trading systems.
  • Growth trajectory: Clear path to increased scope and compensation as the fund scales with institutional AUM.
  • Attractive compensation: Highly competitive base salary and annual bonus that scales as the business grows.
  • Positive, inclusive and encouraging work environment.
Key Skills
PythonSQLData engineeringAirflowSnowflakePandasPolarsSystematic tradingBacktestingMarket data pipelinesAWSDockerCI/CDMachine learningCommoditiesFinancial modeling
Categories
Finance & AccountingSoftwareData & AnalyticsEngineeringScience & Research
Benefits
Competitive base salaryAnnual bonusInclusive work environment
Job Information
📋Core Responsibilities
The Quant Developer will build and maintain data pipelines, backtesting frameworks, and productionize signals into the live trading stack. They will also collaborate with researchers to optimize machine learning models and develop monitoring dashboards for data quality and model drift.
📋Job Type
full time
📊Experience Level
2-5
💼Company Size
17
📊Visa Sponsorship
No
💼Language
English
🏢Working Hours
40 hours
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