Summer 2026 Machine Learning & Quantitative Investment Systems Engineer Intern

About Olive Branch Capital

Olive Branch Capital is a private investment fund that works in the best interest of its investors. Olive Branch Capital focuses on long-term value and growth investments based on real fundamental value. Currently, the innovation team at Olive Branch Capital set out to develop a proprietary Intelligence System, an institutional-grade platform that uses custom machine learning, structured decision frameworks, and compounding memory systems that improves every decision made at the firm.

This is an early-stage, high-impact opportunity. You will not be doing busywork. You will be building core infrastructure for a system designed to compound intelligence over time.

The Role

We are looking for an ML Engineering & Quantitative Investment Systems Intern, someone at the intersection of software engineering, machine learning, and quantitative finance. This role sits at the foundation of the system: building the data pipelines that feed the brain, engineering the features that drive analysis, and contributing to the custom ML models that power the engine of the system being built.

This is not a research internship where you read papers and write summaries. You will write production code that runs in a live system. You will see your work directly impact decisions. And you will learn how institutional-grade ML systems are architected from the ground up.

What You Will Work On

Data Pipeline and Feature Engineering:

• Building and maintaining real time and historical data ingestion pipelines using APIs.
• Develop and improve our custom parser that extracts structure financial data. Work with XBRL/XML formats to reliably extract financial statements across different filing formats.
• Design and implement features for the Feast feature store while enforcing point-in-time correctness to prevent lookahead bias.
• Build automated data quality checks, freshness monitoring, and anomaly detection on incoming data streams. Create dashboards for data pipeline health.

Machine Learning and Analysis:

• Assist with training our proprietary model and help with data preparation, hyperparameter tuning, and evaluation.
• Contribute to the analysis pipeline, fine tuning encoder models for deeper analysis.
• Help build and validate the walk-forward back testing infrastructure. Implement calibration tests, performance attribution, and regime-conditional analysis.
• Contribute to the Monte Carlo simulation framework for generating probability-weighted scenario distributions with correlated risk factors.

Infrastructure and Tooling:

• Build FastAPI endpoints that serve analysis results, feature data, and decision records to the frontend.
• Work with PostgreSQL/TimescaleDB for time-series data, MongoDB for document storage, and Neo4j for the knowledge graph. Write efficient queries and maintain schema integrity.
• Write unit and integration tests. Maintain test coverage standards. Help build the continuous integration pipeline with GitHub Actions.

Who You Are

Required

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Data Science, Quantitative Finance, Financial Engineering, Statistics, Mathematics, or a related quantitative field.
• Strong Python programming skills. You can write clean, tested, production-quality code, not just Jupyter notebook prototypes. You understand data structures, algorithms, and software engineering principles.
• Experience with data manipulation and analysis using pandas, NumPy, or similar libraries. You are comfortable working with large, messy datasets.
• Familiarity with SQL and relational databases. You can write complex queries and understand schema design.
• Basic understanding of machine learning concepts: supervised learning, training/validation/test splits, overfitting, feature engineering, model evaluation. You do not need to be an ML researcher, but you should understand the fundamentals.
• Git proficiency. You use version control in your own projects and understand branching, pull requests, and code review.
• Self-directed and comfortable working independently. This is a small team. You will be given clear objectives but will need to figure out the implementation details yourself (with guidance).

Preferred

• Experience with PyTorch or TensorFlow. You have trained a model from scratch (even a simple one) and understand the training loop, loss functions, and optimization.
• Familiarity with financial data and concepts: financial statements, valuation ratios (P/E, EV/EBITDA, FCF yield), market data (OHLCV), and basic accounting. You do not need to be a CFA, but you should know what a balance sheet is.
• Experience with Docker and containerized development environments. You have used Docker Compose to run multi-service applications locally.
• Exposure to FastAPI, Flask, or Django for building web APIs.
• Experience with time-series data or event-driven architectures.
• Active interest in financial markets. You follow the markets, have opinions about companies, and understand what an investment thesis is.

Bonus

• Experience with NLP, transformer architectures, or text classification.
• Familiarity with Apache Kafka, Redis, or Celery for distributed systems.
• Experience with graph databases (Neo4j) or knowledge graph construction.
• Contributions to open-source projects in data science, ML, or fintech.
• Previous internship at a fintech company, hedge fund, or data engineering team.

How to Apply

Send us the following:

• Resume: Standard format. Highlight relevant coursework, projects, and technical skills.
• Code Sample: Share a representative code sample (a few hundred lines of clean, documented Python or a data pipeline you have built - feel free to share your github profile).
• Short Write-up(1 page max): Pick a publicly traded company. In one page, describe why it is either overvalued or undervalued right now. Include at least one quantitative data point supporting your thesis. We are not looking for a perfect analysis, we are looking for structured thinking, intellectual curiosity, and the ability to form and defend a thesis.

Please put the mentioned documents all in one PDF file and upload it.

Job Types: Full-time, Part-time, Internship / Co-op
Contract length: 12 weeks

Pay: $36,000.00-$65,000.00 per year

Benefits:

• Paid time off
• Work from home

Work Location: Hybrid remote in Burlington, ON (Halton District)