Quant Developer - Systematic Commodities Hedge Fund

Moreton Capital Partners; Ciudad de México; On-site; MXN 900,000 - 1,300,000; Full time

Job summary

Moreton Capital Partners is seeking a Quant Developer to join our team, building data pipelines and LLM-based workflows for research and production. You will enhance backtesting, implement cloud and local ML execution, and productionize signals in a fast-moving capital environment.

You will collaborate with researchers and traders, deploy in AWS/Docker environments, and contribute to research tooling and dashboards while maintaining robust, testable code.”

Qualifications

• Bachelor's in CS/Comp-Eng or computationally heavy subject.
• Ideally a minor in Finance.
• Experience with data engineering and ML workflows is beneficial.

Responsibilities

• Build and maintain data pipelines ingesting futures and alternative datasets.
• Build and improve LLM-based workflows that support research, data processing, and internal tooling.
• Improve backtesting framework (event-driven simulations, realistic slippage/costs, walk-forward validation).
• 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.

Skills

Python

SQL

Data pipelines

Backtesting

Autonomy

Education

Bachelor's in CS/Comp-Eng or equivalent

Finance minor (preferred)

Tools

Airflow

Snowflake

Pandas

Polars

AWS

Docker

CI/CD

MLflow

Weights & Biases

Git

Job description

Quant Developer – Systematic Commodities Hedge Fund

Moreton Capital Partners is seeking a talented Quant Developer to join our team. We are 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 and alternative datasets (from price data from vendor feeds to unstructured data).
• Build and improve LLM-based workflows that support research, data processing, and internal tooling.
• 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 is already live trading 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.