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Machine Learning Engineer at Peak Reservations. Remote Location: Remote. About Peak. At Peak, we're redefining reservations. We don't see reservations as placeholders for a day and time — we see them as tickets to unique experiences.. Created in partnership with The Parker Palm Springs, a Leading Hotel of the World, Peak is built by hospitality experts for hospitality experts. We're a small, remote team of ~10 based in NYC, and we're bringing the Peak experience to one of the top dining scenes in the country.. The Role. We're looking for a Machine Learning Engineer to design and build our dynamic pricing system.. Restaurant tables aren't created equally, and demand isn't static. A corner booth on Saturday night books out weeks in advance. That same table on a Tuesday off-season? Different story. We want pricing that reflects reality.. You'll own the problem end-to-end:. Analyze 4+ years of reservation data from live restaurant partners to understand demand patterns. Build predictive models that account for factors like table type, day of week, time slot, booking lead time, seasonality, and more. Design a dynamic pricing engine that can adjust prices based on real-time demand signals. Work alongside our engineering team to integrate your models into the Peak platform. This is a high-ownership role. We have the data and the engineering support — we need someone who can drive the ML strategy and build something that works.. You Might Be a Good Fit If You.... Are currently pursuing (or recently completed) a degree in Computer Science, Statistics, Applied Math, or a related field — at any level. Have hands-on experience with ML beyond coursework: personal projects, research, Kaggle, internships, etc.. Are comfortable with Python and standard ML tooling (pandas, scikit-learn, PyTorch/TensorFlow, etc.). Have some exposure to time series forecasting, demand modeling, or pricing optimization (a plus, not required). Can communicate clearly with non-technical stakeholders — this isn't a siloed research role. Are excited to work on a real product with real users, not just experiments. Why This Role. Real impact, fast.. Your work goes into production, not a research backlog.. Flexibility.. Part-time hours, fully remote, async-friendly.. Autonomy.. You'll drive technical direction, not just execute on specs handed to you.. Interesting problem.. Dynamic pricing is a well-studied domain (airlines, hotels, ride-sharing) but under-explored in restaurants. You'll be building something genuinely new.