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About This Role
Senior Manager, Data Science and Machine Learning
New York, NY
Squarespace is hiring a Senior Manager, Data Science and Machine Learning to lead the team behind our predictive models, causal inference frameworks, and AI evaluation systems. These are the tools that inform how we acquire customers, keep them, and serve them at scale. Reporting to the VP of Data, you'll lead a team of 12 data scientists and ML engineers doing work that is directly tied to how Squarespace leverages AI and data-driven capabilities. You will work closely with product, engineering, and analytics partners across the organization to introduce new ways to leverage data in our products and decision systems, for customers and internal use. This is an opportunity to step into a critical senior leadership seat and set the direction for how machine learning and data science are practiced at Squarespace.
This is a hybrid role working from our NYC office 3 days per week. You will report to the VP, Data.
You'll Get To...
- Lead and develop a team of 12, including three direct reports who manage their own teams
- Set the technical and strategic direction for predictive modeling, causal inference, and AI evaluation across Squarespace's product and business priorities
- Apply data science to Squarespace's biggest growth levers: marketing measurement, sales growth, product optimization, customer support allocation, and risk and credit modeling
- Partner with product, engineering, and business leaders to translate complex statistical and ML work into decisions and systems that drive customer acquisition, retention, engagement, and growth across our global products.
- Build the systems, processes, and team culture that raise the quality and speed of data science delivery across the organization
- Evolve the team's ML infrastructure and tooling as AI capabilities at Squarespace grow
Who We're Looking For
- 12+ years in data science or machine learning, including experience leading technical teams through complex, high-stakes projects
- 5+ years managing managers, with demonstrated ability to develop senior individual contributors and team leads simultaneously
- Hands-on experience building or overseeing production predictive models, causal inference frameworks, or AI evaluation systems
- Skill at aligning product, engineering, and business partners around a shared roadmap and navigating trade-offs clearly, especially in explaining statistical or technical trade-offs to executives without losing the nuance
- Experience scaling a data science or ML function, including team structure, hiring, and technical standards
Nice to Have
- Experience building or overseeing LLM evaluation harnesses, including golden sets and LLM-as-judge methods
- Familiarity with prompt and model lifecycle management in production
- Experience designing human-in-the-loop review for AI-driven features
- Background in marketing science, experimentation, or credit/risk modeling
Benefits & Perks
- A choice between medical plans with an option for 100% covered premiums including medical, dental, and vision
- Supplemental Life and Disability Insurance plans
- Fertility and adoption benefits
- 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
- 7 Global Employee Resource Groups (ERGs)
- Dog-friendly workplace
- Free lunch and snacks
- Private rooftop
- Hack week twice per year
Cash Compensation Range: $223,000 - $358,800 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 overtime pay), Squarespace employees are eligible to be granted an option to purchase our common stock. Sales positions generally offer a competitive On Target Earnings (OTE) incentive structure in addition to base salary.
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 — from Squarespace and Squarespace Payments, to subsidiaries like Tock, Bio Sites, and Unfold, as well as Acuity Scheduling — works together to help customers stand out and succeed online. By fundamentally changing the way individuals and businesses around the world build an online presence, we empower our customers with a competitive advantage in reaching their audience. The company is headquartered in New York City, with offices in Dublin and Aveiro. For more information about our company, visit https://www.squarespace.com/about-us.
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.
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Salary Context
This $223K-$358K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Squarespace, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($290K) sits 37% above the category median. Disclosed range: $223K to $358K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Squarespace AI Hiring
Squarespace has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, New York, NY. Compensation range: $358K - $358K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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