AI Engineer (NY, New York)

Demand forecasting models that predict occupancy and room-night demand by property, segment, day-of-week, and season — accounting for events, seasonality, and booking pace.

Dynamic pricing and rate-optimization engines that recommend BAR and corporate rates, balancing occupancy, ADR, and RevPAR against competitor positioning.

Account intelligence tooling that ingests internal production data and external signals (M&A activity, headcount trends, travel-budget shifts) to flag growing accounts, at-risk accounts, and uncaptured market opportunity.

Labor and staffing models that forecast labor demand against projected occupancy and arrivals — optimizing schedules, hours, and cost across housekeeping, front desk, F&B, and other departments while protecting service levels.

RFP and negotiation support tools that help the team price contracts, model rate scenarios, and prioritize target accounts during RFP season.

LLM-powered workflows — summarizing market intelligence, drafting account strategy notes, and answering natural-language questions over revenue data.

A testing laboratory for beta technologies — stand up and run a controlled environment where new AI tools and models can be piloted, stress-tested, and validated against real operational data before broader rollout, including the experimentation framework, sandboxed data, and feedback loops with property and commercial teams

Design, train, evaluate, and deploy machine learning models on real booking, rate, and market data.

Build data pipelines that bring together PMS, CRS, booking-channel, and third-party market data into clean, reliable feature sets.

Stand up the infrastructure to serve models in production — APIs, monitoring, retraining, and guardrails.

Operate the beta testing lab — design pilots, recruit internal users, measure results, and decide what graduates to production versus what gets killed.

Partner closely with revenue and commercial teams to translate domain knowledge into product, and to make sure outputs are trustworthy and actionable.

Define and track the metrics that matter — forecast accuracy, recommendation adoption, labor cost and productivity, and downstream revenue impact.

3+ years building and shipping ML systems in production (not just notebooks).

Strong Python and the modern ML/data stack (e.g. pandas, scikit-learn, PyTorch or TensorFlow, SQL).

Solid grounding in forecasting, optimization, or recommendation/pricing problems.

Experience taking models from prototype to deployed service, including monitoring and iteration.

Ability to communicate clearly with non-technical stakeholders and translate business problems into technical ones.

Experience in hospitality, travel, airlines, retail, or another revenue-management-driven industry.

Familiarity with dynamic pricing, demand modeling, or yield/revenue management.

Experience integrating LLMs into applications (RAG, structured extraction, agentic workflows).

Cloud and MLOps experience (AWS/GCP/Azure, containerization, CI/CD for ML).

Comfort with experimentation and causal measurement (A/B testing, uplift modeling).

You'll own meaningful problems end-to-end, see your work move real revenue, and help build a data and AI capability from an early stage. If you want your models to ship and matter rather than sit in a backlog, this is that role.