Founding Machine Learning Engineer, Domains Search

Squarespace•New York, NY

3d•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 leveraging large language models to generate creative domain suggestions, to building scalable ranking and personalization systems, our team sits at the intersection of applied machine learning, natural language processing, and large-scale search & recommendation infrastructure. We are looking for Senior Machine Learning Engineers to build our search platform from the ground up with focuses on retrieval and ranking. You will report to the Engineering Manager of the Domains Search team in our New York City headquarters. Why Join Us This is a unique opportunity to shape a greenfield ML initiative at Squarespace. As part of a small, high-impact team, you'll have influence over how we build our search platform and how millions of customers discover their online identity. If you've wanted to build a next generation of search system with few legacy constraints, we'd love to hear from you. You'll Get To... Be one of our founding MLEs, owning key parts of this scope (retrieval or ranking) and contributing across the stack as the team grows. Design, build, and deploy hybrid search systems (classical ML + genAI + rule-based) for generative domain name search which balance relevance, latency, and scale. Lead technical direction in retrieval (index design, query parsing, retrieval strategies) or ranking (learning-to-rank, multi-objective optimization). Lead embedding model selection, post-training optimization, and evaluation to power both semantic retrieval and ranking features. Collaborate with engineers, data scientists, and product managers to translate ambiguous product ideas into technical approaches (including build vs. buy decisions).

Requirements

5+ years of professional experience in machine learning engineering, with at least 2 years focused on search, retrieval, ranking, or recommendation systems.
Experience building production search or ranking systems end-to-end, whether standing up new systems or leading architectural shifts.
Depth in two or more of: retrieval (indexing, vector search), ranking (learning-to-rank, multi-objective optimization), or embeddings. LLM integration is a plus.
Comfort owning strategy to help shape our roadmap while building high-quality technical solutions.
Strong architectural judgment, with an ability to evaluate tradeoffs and communicate decisions clearly to both technical and non-technical stakeholders.
Proven ability deploying and operating ML systems at scale, ensuring reliability, performance, and quality under high traffic volumes.

Responsibilities

Be one of our founding MLEs, owning key parts of this scope (retrieval or ranking) and contributing across the stack as the team grows.
Design, build, and deploy hybrid search systems (classical ML + genAI + rule-based) for generative domain name search which balance relevance, latency, and scale.
Lead technical direction in retrieval (index design, query parsing, retrieval strategies) or ranking (learning-to-rank, multi-objective optimization).
Lead embedding model selection, post-training optimization, and evaluation to power both semantic retrieval and ranking features.
Collaborate with engineers, data scientists, and product managers to translate ambiguous product ideas into technical approaches (including build vs. buy decisions).