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Moreton Capital Partners

Software Engineer - Systematic Commodities Hedge Fund

LocationMexico City, Mexico City, Mexico
Typefull-time
SeniorityMid
Company size9+ people
PostedOct 6, 2026 · 6d ago
Verified live1h ago
At a glanceSummarised by Seekless from the posting.
Must have9
Strong Python and SQL skills
Experience with Pandas or Polars
Experience building APIs with FastAPI or similar Python frameworks
Good understanding of relational databases and data modeling
Experience building data pipelines or other data-intensive applications
Comfortable with Git, testing, debugging, and maintaining production code
Comfortable working across different parts of the stack and learning new technologies as needed
Self-starter with strong problem-solving skills, attention to detail, and the ability to work autonomously in a lean, high-ownership environment
Bachelor's degree in Computer Science, Computer Engineering, or another computationally intensive field
Nice to have5
Experience with React, TypeScript, or other modern front-end technologies
Experience with Snowflake and Airflow or another orchestration framework
Familiarity with AWS, Docker, and CI/CD
Experience with LLMs, agents, RAG, or AI APIs
Experience with financial markets, commodities, systematic investing, or time-series data
Skills
Python
SQL
Pandas
Polars
FastAPI
Git
Snowflake
React
TypeScript
Airflow
AWS
Docker
CI/CD
LLMs
RAG
About the role
We are seeking a talented Software Engineer to join our team. We are live trading across global commodity futures, supported by an investment process rooted in machine learning.
This is a broad engineering role and a unique opportunity to work directly with our global team, owning infrastructure that supports research, trading, and investment decision-making in a fast-moving, real-capital environment.
Key Responsibilities
•
Build and maintain data pipelines ingesting market and alternative datasets, from vendor feeds and price data to unstructured data.
•
Build backend services and APIs supporting internal applications and research workflows.
•
Develop internal systems for trading, portfolio management, P&L, risk, research, and operations.
•
Design and maintain data models and warehouse infrastructure using SQL and Snowflake.
•
Integrate applications with internal systems, data providers, and third-party APIs.
•
Build and improve LLM-based workflows, AI-enabled tools, agents, and structured data extraction.
•
Develop data-quality checks, monitoring, and alerting to improve reliability and data integrity.
•
Work closely with Quant Researchers and the CIO to translate research and business requirements into production systems.
•
Improve testing, documentation, code quality, and engineering practices across the team.
Requirements
Strong Python and SQL skills; clean, testable code is a must.
Experience with Pandas or Polars.
Experience building APIs with FastAPI or similar Python frameworks.
Good understanding of relational databases and data modeling.
Experience building data pipelines or other data-intensive applications.
Comfortable with Git, testing, debugging, and maintaining production code.
Comfortable working across different parts of the stack and learning new technologies as needed.
Self-starter with strong problem-solving skills, attention to detail, and the ability to work autonomously in a lean, high-ownership environment.
Bachelor’s degree in Computer Science, Computer Engineering, or another computationally intensive field.
Bonus points for
•
Experience with React, TypeScript, or other modern front-end technologies.
•
Experience with Snowflake and Airflow or another orchestration framework.
•
Familiarity with AWS, Docker, and CI/CD.
•
Experience with LLMs, agents, RAG, or AI APIs.
•
Experience with financial markets, commodities, systematic investing, or time-series data.
Benefits
Impact from day one: Build mission-critical systems directly used by our investment and research teams.
Direct exposure: Work alongside the CIO and Quant Researchers, with a direct line to decision-making and fast feedback on what you build.
Broad ownership: Work across data, backend systems, internal applications, AI tooling, and trading infrastructure rather than being limited to one narrow area.
Learning curve: Deep exposure to commodity markets, machine-learning research workflows, and institutional-grade trading systems.
Growth trajectory: Clear path to increased scope, responsibility, and compensation as the fund scales.
Attractive compensation: Highly competitive base salary and annual bonus that scales with the business.
Positive, inclusive, and encouraging work environment.
More roles at Moreton Capital Partners
Machine Learning Researcher - Systematic Commodities Hedge FundQuantitative Analyst - Prediction MarketsTraders - Prediction MarketsQuantitative Researcher / Signal Generator - Alpha Signal ProgramPortfolio Manager - Prediction Markets
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