Squarespace's Domains Search team is building the next generation of intelligent search experiences to help customers find the perfect domain, applying large language models to produce creative domain suggestions, and building scalable ranking and personalization systems.
Requirements
- Experience with NLP and LLMs (prompt engineering, fine-tuning, or integration into production systems).
- Proficiency in Python, ML frameworks (TensorFlow, PyTorch), and data tooling (Spark, SQL).
- Experience deploying ML models to production and scaling them to handle large traffic volumes.
- Familiarity with A/B testing and online experimentation methodologies.
- Strong background in search, ranking systems, or recommendation engines.
Responsibilities
- Design, build, and deploy ML models for domain search, including ranking, personalization, and generative domain name creation.
- Integrate and fine-tune LLMs and hybrid systems (LLMs + rule-based + classical ML) to create brandable, contextual, and internationalized domain suggestions.
- Develop and evaluate ranking algorithms that optimize for multiple signals such as relevance, uniqueness, brandability, and business goals.
- Implement personalization approaches leveraging user, account, and demographic data to tailor search results.
- Own end-to-end ML lifecycle: from feature engineering, model training, and evaluation to deployment, monitoring, and iterative improvement.
- Contribute to experimentation frameworks (A/B testing, Statsig, etc.) to measure model impact and continuously improve user experience.
- Ensure search quality at scale by developing robust evaluation pipelines and metrics.
Other
- 5+ years of professional experience in machine learning engineering, with at least 2 years at a senior/lead IC level.
- Collaborate with engineers, data scientists, and product managers to translate ambiguous product ideas into technical requirements and scalable ML solutions.
- LI-Hybrid
