Peak Reservations is hiring a Machine Learning Engineer to design and build the dynamic pricing system for our restaurant reservation platform. You will own this problem end-to-end, from analyzing historical data to integrating predictive models that drive real-time pricing decisions.
What You'll Do
- Analyze 4+ years of reservation data from live restaurant partners to understand demand patterns.
- Build predictive models accounting for factors like table type, day of week, time slot, booking lead time, and seasonality.
- Design a dynamic pricing engine that adjusts prices based on real-time demand signals.
- Work alongside the engineering team to integrate models into the Peak platform.
What We're Looking For
- Currently pursuing or recently completed a degree in Computer Science, Statistics, Applied Math, or a related field.
- Hands-on experience with ML beyond coursework: personal projects, research, Kaggle, internships, etc.
- Comfortable with Python and standard ML tooling (pandas, scikit-learn, PyTorch/TensorFlow, etc.).
- Can communicate clearly with non-technical stakeholders.
- Excited to work on a real product with real users.
Nice to Have
- Exposure to time series forecasting, demand modeling, or pricing optimization.
Technical Stack
- Python
- pandas
- scikit-learn
- PyTorch/TensorFlow
Team & Environment
You will join a small, remote team of ~10. We emphasize real impact and autonomy—your work goes directly into production and you'll drive technical direction. You will be tackling a genuinely new problem in restaurant dynamic pricing.
Work Mode
This is a fully remote position.
