Quantitative Research & ML Engineering Internship (Summer Hackathon Cohort)
Company: Crypt0nest.io (A Tekly Studio Innovation Lab)
Location: Remote (U.S. preferred but not required)
Duration: 8–12 weeks (Summer Cohort, targeting May 15 start)
Time Commitment: 10–20 hours/week
Compensation: Unpaid (Educational/Project-based, for-credit or certificate available)
About the Program
Crypt0nest.io is an early-stage investment intelligence platform building predictive systems for digital assets. This summer, we are hosting an intensive, autonomous Machine Learning Hackathon designed for high-curiosity individuals who are ready to build inside a production-grade ML ecosystem.
Rather than fetching coffee, interns in our lab are granted access to our proprietary, containerized ML backtesting framework. You will use proprietary datasets to learn how to build, validate, and evaluate quantitative trading models. This is an asynchronous, project-based educational environment designed to bridge the gap between academic theory and real-world ML engineering.
This program is designed as a performance-based evaluation funnel. Participants will be ranked based on their ability to execute end-to-end ML workflows, from data processing to model validation and backtesting. Top performers will be considered for extended roles with Crypt0nest.
What You’ll Do
You will work entirely within our Machine Learning track, navigating a fully documented, end-to-end quant pipeline.
- Quantitative Research: Develop and test systematic trading signals using historical time-series data.
- Machine Learning: Build predictive models using tree-based methods and hybrid architectures, focusing on strict data validation (avoiding look-ahead bias and data leakage).
- Feature Engineering: Engineer and test features across price, volatility, and macro factors using pandas and NumPy.
- Code Collaboration: Submit your strategies via GitHub Pull Requests, learning the rigorous standards of professional code review.
- End-to-End Execution: Complete the full ML pipeline from raw data ingestion through backtesting and evaluation, demonstrating practical, working outputs.
Required Skills
- Technical: Strong Python skills, experience with the Python standard library, as well as data analysis libraries (NumPy, Pandas, Scikit-learn).
- Foundational Knowledge: Basic understanding of ML modeling and a strong desire to learn quantitative finance concepts (Sharpe, Sortino, drawdowns).
- Independence: Ability to thrive in an asynchronous, self-guided environment using comprehensive technical documentation and video guides.
- Soft Skills: Strong problem-solving mindset and clear communication via Slack and GitHub.
What You’ll Gain
- Practical Experience: Hands-on exposure to a production-grade ML architecture, purged walk-forward cross-validation, and pipeline automation.
- Portfolio Artifacts: End-of-program deliverables you can showcase to future employers, including a completed, documented ML trading strategy evaluated in a simulated backtest environment.
- Performance-Based Opportunity: Top-performing participants, as measured by execution, code quality, and model outcomes, will be considered for ongoing roles, leadership positions, or extended collaboration with Crypt0nest.
How You’ll Be Evaluated
- Completion of the full ML pipeline
- Quality and rigor of submitted code (via PR review)
- Model performance and robustness (Sharpe, drawdown, stability)
- Consistency of engagement and progress updates
How to Apply
Submit the following via our application form: 📎 Resume or LinkedIn profile 🔗 GitHub or portfolio (if available) ✍️ A short note (100–200 words) on what you hope to learn this summer
Apply here: https://talent.flowmingo.ai/jobs/summer-internship-may-2026-1
Interview here: https://talent.flowmingo.ai/interview/47e685e8-bc41-482b-8efc-b97531acce3c?project_id=7fdaf70d-c58c-4994-8efa-51503aed1556