Machine Learning Engineer

Dorsia•New York, NY

8d•$100,000 - $250,000•Onsite

About The Position

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.

Requirements

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)
Ability to reason about data and metrics—and a drive to tie models to real business outcomes
5 days a week in our SoHo NYC office

Nice To Haves

Experience working on recommender systems, reinforcement learning, graph-based models, marketplace dynamics, or pricing systems
Understanding of retrieval systems, embeddings, or vector search
Experience in luxury, hospitality, or marketplace products
Familiarity with feature stores, and observability tools
Startup mindset: high agency, comfort with ambiguity, bias to ship

Responsibilities

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.
Use AI to Ship Faster: Leverage generative AI and LLMs to accelerate development, boost internal tooling, and augment user experiences.
Collaborate Across Functions: Partner closely with product, design, and ops to ship delightful and impactful ML features.