Senior Machine Learning Engineer, Domains Search at Squarespace

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

11 Feb, 26

Salary

236500.0

Posted On

13 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Natural Language Processing, Large Language Models, Ranking Systems, Recommendation Engines, Python, TensorFlow, PyTorch, Data Tooling, Spark, SQL, A/B Testing, Model Deployment, Feature Engineering, Model Training, Model Evaluation

Industry

Software Development

Description

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. You'll Get To... 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. Who We're Looking For 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. Benefits & Perks A choice between medical plans with an option for 100% covered premiums Fertility and adoption benefits Access to supplemental insurance plans for additional coverage Headspace mindfulness app subscription Global Employee Assistance Program Retirement benefits with employer match Flexible paid time off 12 weeks paid parental leave and family care leave Pretax commuter benefit Education reimbursement Employee donation match to community organizations 8 Global Employee Resource Groups (ERGs) Dog-friendly workplace Free lunch and snacks Private rooftop Hack week twice per year Cash Compensation Range: $128,500 - $236,500 USD The base salary for this position will vary based on job-related criteria including relevant skills, experience, and location, among other factors. In addition to the cash compensation above (which includes base salary and, where applicable for eligible roles, may include on-target commissions or overtime pay), Squarespace employees are eligible to be granted an option to purchase our common stock. About Squarespace Squarespace is a design-driven platform helping entrepreneurs build brands and businesses online. We empower millions of customers in more than 200 countries and territories with all the tools they need to create an online presence, build an audience, monetize, and scale their business. Our suite of products range from websites, domains, ecommerce, and marketing tools, as well as tools for scheduling with Acuity and creating and managing social media presence with Bio Sites and Unfold. Our team of more than 1,500 is headquartered in bustling New York City, with offices in Dublin, Ireland, Aveiro, Portugal, and coworking spaces in the UK, Netherlands, and Australia. For more information about our company culture, visit https://www.squarespace.com/about/careers. Our Commitment Today, more than a million people around the globe use Squarespace to share different perspectives and experiences with the world. Not only do we embrace and celebrate the diversity of our customers, but we also work toward the same in our employees. At Squarespace, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, national origin, gender, sex, gender identity or expression, sexual orientation, age, citizenship, marital or parental status, disability, veteran status, or other class protected by applicable law. We are proud to be an equal opportunity workplace. #LI-Hybrid Thank you in advance for providing the following details about your work history from your resume! This helps us ensure that your candidate information is accurate and consistent during the hiring process. Squarespace will never solicit your personal banking information or ask you to transfer money in connection with a job offer or interview. We also will not reach out to you via phone or SMS without your permission or knowledge.

Responsibilities

Design, build, and deploy ML models for domain search, including ranking, personalization, and generative domain name creation. Collaborate with engineers, data scientists, and product managers to translate ambiguous product ideas into technical requirements and scalable ML solutions.