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About This Role
*This is not a remote position; this role requires onsite work in NYC office 3 days a week.*
Overview
The New York Public Library has been an essential provider of free books, information, ideas, and education for all New Yorkers for over 125 years. Founded in 1895, NYPL is the nation's largest neighborhood public library system and the most used research library in the world. NYPL provides a fundamental resource to millions of readers, learners and scholars. The Library’s portfolio of digital products and platforms is the core linchpin in how our patrons near and far can access information to create new content, support their personal learning and build stronger communities.
The New York Public Library (NYPL) is seeking an inspiring and technically adept Director of Engineering, Data and AI to lead our data and artificial intelligence initiatives. This critical role is responsible for overseeing the development, and operation of NYPL's enterprise data platforms and driving the in-house development of cutting-edge AI-powered search and discovery products. Our core objective is to develop and deploy these powerful tools in a responsible and ethical manner. This role’s goal is to empower researchers, scholars, and the general public to unearth and engage with materials within NYPL's vast and diverse collections in impactful ways. It will also seek to improve these discovery processes via the robust collection and management of data, furthering NYPL’s ability to understand its patrons and improve its offerings to them.
We are looking for someone we can count on to:
Own:
- Technical roadmap and execution for our Data and AI products
- Strategic objectives for the implementation and adoption of AI and Data driven initiatives across NYPL
- Hiring, training, coaching and management of engineering team members
- Continuous improvement of Engineering practices
- Our tech stacks, frameworks, vendor tools, and processes
- Identification and mitigation of risk
Teach:
- How to develop and implement data and AI strategies that directly support measurable business outcomes
- Critical and strategic thinking skills, cross-functional collaboration, and technical decision-making abilities to scale the team's impact.
- Champion the ethical and responsible use of AI and data, including bias mitigation, transparency, and the potential impact on patrons and society
- New technologies, such as AI-enabled engineering tools to improve workflows and data management tools
Learn:
- NYPL culture, goals, strategy and the environment in which we work
- The specific challenges that digital technology introduces for our staff, patrons, vendors, partners and the Library as a whole
- Stay up to date on the latest developments in AI/ML technology, as well as ongoing developments in data management technologies and platforms
Improve:
- Remove roadblocks and advocate for the changes needed to maintain a world-class engineering team
- Help NYPL embrace data-driven transformation and help the organization better leverage data in its business practices
- Ensure that the team’s time and capacity are adequately allocated in order to deliver on high-priority projects in a timely manner
- Socialize core engineering metrics so that the organization better understands Engineering’s internal objectives and results
- Break down silos between individual engineers, and between engineering teams and key partners.
Responsibilities:
Technical Strategy and Operation
- Identify, evaluate, and implement emerging technologies, algorithms, and methodologies into our products and services
- Define and champion the technical vision and roadmap for NYPL's data platforms, enterprise analytics capabilities, and the AI Search and Discovery products.
Leadership and People Management
- Directly manage and mentor a team of Tech Leads and senior engineers, cultivating their leadership skills, business acumen, and technical decision-making.
- Own the hiring, training, and coaching process for Engineering team members, fostering a culture of innovation and continuous improvement.
- Set clear goals and metrics for software development teams and maintain high standards of software quality while delivering on project goals.
Engineering Practices and Architecture
- Own the continuous improvement of Engineering practices, patterns, and processes, removing roadblocks to maintain a world-class engineering team.
- Evaluate emerging technologies and industry trends (including AI-enabled engineering tools) and incorporate them into the organization’s practices where appropriate.
- Drive the resolution of complex technical challenges and lead efforts to improve engineering processes.
Required Education, Experience & Skills
Required Education and Certifications
- Bachelor’s degree, or equivalent experience/application
Required Experience
- Minimum of 10+ years of experience in data engineering, software engineering, or machine learning engineering, with at least 3-5 years in a leadership/management role.
- 5 -10 years of progressive leadership/management experience.
- Drive continuous improvement in AI methodologies and best practices.
- Demonstrates good judgement in handling situations with multiple good solutions, or situations with no good solution
- Proactive mindset that solves future problems before they become emergencies.
Required Skills
- Strong technical understanding of AI/ML DevOps, evaluation frameworks, agentic workflows, and permission systems integration. Proven ability to collaborate closely with technical leads and data scientists.
- Deep expertise in designing, building, and operating large-scale, production-grade data platforms and pipelines (SQL/NoSQL, cloud data warehousing like Snowflake, Databricks).
- Production experience in leading an AI/ML engineering team to deliver a product, specifically involving NLP, vector databases, and RAG architectures.
- Production experience working with data stores, including ElasticSearch and/or Solr, with vector databases/stores a plus
- Deep understanding of software development best practices, including DevOps best practices around CI/CD, git workflows, testing/test automation and infrastructure as code (IaC)
- Familiarity with cloud infrastructure, with experience on AWS a plus
Managerial/Supervisory Responsibilities:
- Manages a diverse technology/developer team (2-6 tech leads and engineers)
More...
Core Values
*All team members are expected and encouraged to embody the NYPL Core Values:*
- Be Helpful to patrons and colleagues
- Be Resourceful in solving problems
- Be Curious in all aspects of your work
- Be Welcoming and Inclusive
Work Environment
- Office environment
- Hybrid work environment; required work in the office in NYC 3 days a week
Physical Duties
- N/A
Pre-Placement Physical Required?
- No
Union/Non Union
- Non-Union
FLSA Status
- Exempt
Schedule
- Monday - Friday; 9-5
- Hybrid work environment; required work in the office in NYC 3 days a week
*This job description represents the types and levels of responsibilities that will be required of the position and shall not be construed as a declaration of all of the specific duties and responsibilities for the role. Job duties may change if Library priorities change. Employees may be directed to perform job-related tasks other than those specifically presented in this description as needed.*
*The New York Public Library**Salary Statement*
At the Library, we believe that pay transparency and pay equity are important to ensuring we source the best candidates and keep the best employees. When making a determination as to the appropriate salary for a candidate, we consider a variety of factors such, including, but not limited to, the position requirements, the skills, prior experience, and educational background required or preferred for the job, the scope and impact of the role within the organization, internal peer equity, and the candidate's specific training, experience, education level, and skills. No single factor is conclusive; the Library reserves the right to consider any and all relevant factors and make a decision consistent with its policies.
Union Salaries are determined by collective bargaining agreement(s).
About The New York Public Library
The New York Public Library is a free provider of education and information for the people of New York and beyond. With 92 locations—including research and branch libraries—throughout the Bronx, Manhattan, and Staten Island, the Library offers free materials, computer access, classes, exhibitions, programming and more to everyone from toddlers to scholars, and has seen record numbers of attendance and circulation in recent years. The New York Public Library serves more than 18 million patrons who come through its doors annually and millions more around the globe who use its resources at www.nypl.org.
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 37,339 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At The New York Public Library, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $252,000 based on 337 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($172K) sits 32% below the category median. Disclosed range: $160K to $185K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
The New York Public Library AI Hiring
The New York Public Library has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Manhattan, NY, US. Compensation range: $185K - $185K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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