Senior Machine Learning Engineer, Domains Search

Squarespace•New York, NY

39d•Hybrid

About The Position

The Domains Search team at Squarespace is building the next generation of intelligent search experiences for helping customers find the perfect domain. From applying large language models to produce creative domain suggestions, to building scalable ranking and personalization systems, our team is at the intersection of applied machine learning, natural language processing, and large-scale search infrastructure. We are looking for a Senior Machine Learning Engineer to help shape and build this platform from the ground up. You will report to an Engineering Team Lead in our New York City headquarters. Why Join Us This is a unique opportunity to shape a greenfield ML initiative at Squarespace. You'll be part of a small, high-impact team building the foundation of how millions of customers discover and secure their online identity.

Requirements

6+ years of industry experience in machine learning engineering, with at least 2 years at a senior/lead IC level.
Strong background in search, ranking systems, or recommendation engines.
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.

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.
Collaborate with engineers, data scientists, and product managers to translate ambiguous product ideas into technical requirements and scalable ML solutions.
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.