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Salary: USD 100,000 - 250,000 / annual
Dorsia is a hospitality tech platform revolutionizing how people experience dining through exclusive restaurant reservations and VIP experiences. We're seeking a Machine Learning Engineer to own the development of intelligent systems powering core product features, personalization, and operational efficiency.
You'll work across the full ML stack—from data pipelines and model training to inference infrastructure and product integration. This is a hands-on, full-stack role with direct impact on how members discover experiences, how restaurants manage demand, and how the business scales.
Key responsibilities include:
- Building ML-powered features for search, ranking, recommendations, pricing, fraud detection, and demand prediction
- Owning projects end-to-end: data sourcing, feature engineering, model deployment, and monitoring
- Deploying real-time inference pipelines and batch workflows using modern cloud-native infrastructure
- Leveraging generative AI and LLMs to accelerate development and augment user experiences
- Collaborating closely with product, design, and operations teams to ship impactful ML features
The role requires 5 days per week in the SoHo NYC office. Dorsia emphasizes in-person collaboration, high-performance culture, and a startup mindset with bias toward shipping.
Tech stack includes Python, TypeScript, PHP; PyTorch, Hugging Face, scikit-learn, LangChain; dbt, PostgreSQL, Redis, Airflow, Metabase; and AWS, Cloudflare, Vercel, Terraform, GitHub Actions.
REQUIREMENTS:
Must-haves:
- 5–10 years of experience in software or ML engineering
- Proven experience building, shipping, and scaling ML models in production (NLP, ranking, classification, etc.)
- Strong programming skills in Python and SQL with understanding of software and data engineering best practices
- 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 with drive to tie models to real business outcomes
- Availability for 5 days per week in-office in SoHo NYC
Nice-to-haves:
- Experience with recommender systems, reinforcement learning, graph-based models, marketplace dynamics, or pricing systems
- Understanding of retrieval systems, embeddings, or vector search
- Background in luxury, hospitality, or marketplace products
- Familiarity with feature stores and observability tools
- Startup mindset: high agency, comfort with ambiguity, bias to ship
