Startups · AI

Machine Learning Engineer

Dorsia · New York City · On-site

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About Dorsia

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About the role

We’re looking for a Machine Learning Engineer to join our growing team and own the development of intelligent systems that power core product features, personalization, and operational efficiency. You’ll work across the stack—from data pipelines and model training to inference infrastructure and product integration. This is a hands-on, full-stack ML role with a direct line to impact—shaping how members discover experiences, how restaurants manage demand, and how our business scales.

What they're looking for

  • 5–10 years of experience in software or ML engineering
  • Experience building,shipping and scaling ML models in production (NLP, ranking, classification, etc.)
  • Strong programming skills in Python and SQL and a good understanding of best practices in software and data engineering
  • Familiarity with ML tooling (PyTorch, TensorFlow, scikit-learn), orchestration (Airflow, dbt), and deployment
  • Experience with cloud services (AWS, GCP, or similar)
  • 5 days a week in our SoHo NYC office
More about this role

Dorsia is at the forefront of hospitality tech innovation. We are revolutionizing the way people experience dining by leveraging cutting-edge technology to offer exclusive restaurant reservations and VIP experiences. Join us as we continue to expand our footprint and reshape the hospitality industry.

We’re looking for a Machine Learning Engineer to join our growing team and own the development of intelligent systems that power core product features, personalization, and operational efficiency. You’ll work across the stack—from data pipelines and model training to inference infrastructure and product integration.

This is a hands-on, full-stack ML role with a direct line to impact—shaping how members discover experiences, how restaurants manage demand, and how our business scales.

  • Build ML-Powered Features

Train and deploy models for search, ranking, recommendations, pricing, fraud detection, demand prediction, and more.

  • Work Across the ML Lifecycle

Own projects from end-to-end, covering data sourcing and feature engineering to model deployment and monitoring.

  • Deploy at Scale

Build real-time inference pipelines and batch workflows using modern cloud-native infrastructure.

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Read the full posting on Dorsia's site ↗

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