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About This Role
Position: Product Engineer, AI & Automation
Department: Digital Experience
Role Overview: The Operational Efficiency team works with departments across the organization to reduce manual effort in daily operations. This role is the team's primary builder. You will take processes that are currently slow, repetitive, or error-prone and turn them into working automations, applying AI capabilities where they fit the problem.
Most of your time will be spent building: writing code, connecting systems, designing and testing agents, and maintaining what you have already shipped. Requirements will reach you as problems rather than finished specifications, so you will work directly with the teams whose processes you are automating to understand how the work is performed before deciding what to build.
We are looking for someone with five or more years of experience who has delivered automations that people depend on daily and who has supported them over time.
What you’ll get to do:
Build
- Design and build automations across operational workflows, including data movement, document handling, reporting, intake and routing, and notifications.
- Connect existing systems using REST APIs, webhooks, scheduled jobs, and integration platforms, working within the organization's current technology stack wherever possible.
- Build components that use large language models where they are the right tool for the problem, including extraction from unstructured documents, classification, routing, summarization, and drafting. Design the prompts, test them against representative data, and define the accuracy required before launching.
- Determine where human review is required, set confidence thresholds that route uncertain cases to staff, and define what happens when the model produces incorrect output.
- Handle supporting work with the same care as the build itself, including data validation, idempotency, credential management, environment separation, and documentation sufficient for another engineer to take over.
Define and scope
- Work with department leads to document current-state processes and identify automation candidates, quantifying the time and cost each process consumes today.
- Assess feasibility and recommend alternatives when a process change or a configuration change in an existing system would solve the problem more effectively than a new automation.
- Translate operational needs into technical requirements and divide work into phases that deliver value early.
- Set clear expectations with stakeholders on accuracy, exception handling, and scope. This matters especially for AI-assisted steps, where acceptable error rates should be agreed in advance.
Own and maintain
- Own each automation through its full lifecycle, including monitoring, response when upstream systems change, iteration as processes evolve, and retirement once it is no longer needed.
- Build in monitoring and alerting so that failures surface quickly instead of going unnoticed.
- Maintain documentation and runbooks so that support does not depend on a single person.
- Track and report impact, including hours returned, error rates, cycle time, and usage.
Support and enable others
- Train and support staff who want to build simple automations on their own, extending the reach of the team.
- Contribute to the team's standards and patterns, including guidelines on what organizational data may be sent to external AI services.
Qualifications
Required
- 5+ years of professional experience building automations, integrations, or internal tools.
- Strong Python, JavaScript, or TypeScript skills, with experience writing code that other engineers have maintained.
- Substantial experience with REST APIs, authentication patterns, webhooks, and system-to-system integration.
- Hands-on experience building with large language models, including prompt design, structured output, and methods for evaluating output quality.
- Working knowledge of SQL and experience with inconsistent or incomplete operational data.
- Proven ability to work with non-technical stakeholders, including gathering requirements, managing expectations, and supporting adoption.
- Good judgment about which processes are appropriate for candidates for automation, and which are not.
Preferred
- Experience with integration or workflow platforms such as Workato, Make, n8n, Zapier, Power Automate, Airflow, or Temporal.
- Experience with document processing, OCR, or extraction from unstructured sources.
- Experience in a comparable operational environment where automations touch finance, HR, or customer-facing workflows.
- Familiarity with data governance and access controls in environments that handle sensitive records. What is Lincoln Center for the Performing Arts?
Lincoln Center for the Performing Arts, Inc. (LCPA) is a cultural and civic cornerstone of New York City. We believe the arts play an important role in our lives: they nourish our hearts and minds, teach us valuable lessons and critical skills, and help us create community. We are resolved not to lose sight of what connects us, and we hope you will consider joining our talented, diverse team.
We are:
- The manager of the 16-acre Lincoln Center campus
- We are one of eleven amazing resident arts organizations
- A leading Arts Presenter.
- We curate numerous programs and performances, showcasing music, dance, and more
- An Education Hub.
- We have reached 20 million students, educators, principals, and community members
Who are our people?
LCPA is a team of dreamers, collaborators, and entrepreneurs who use unique platforms in the heart of New York City and beyond to advocate for the transformative impact of artistic experiences.
Lincoln Center People imagine and create in concert with this mission by founding President John D. Rockefeller III - "The arts are not for the privileged few, but for the many. Their place is not on the periphery of daily life, but at its center."
We welcome applicants from all sectors who agilely solve problems, show up as they are, and can't stop innovating.
What’s the news?
Recent and Upcoming Programming
- Lincoln Center Reveals 25/26 Season Featuring Jeanine Tesori as Visionary Artist
- Kyle Abraham's 20 Years of Urgency at Lincoln Center Rose Theatre
Campus Happenings
- Lincoln Center Gets $50 Million Gift to Promote Contemporary Dance
- The new JFK terminal will show iconic artworks from MoMA, the Met and Lincoln Center
Legacies of San Juan Hill
- WNYC: Lincoln Center Celebrates 'Legacies of San Juan Hill
- Lincoln Center Launches San Juan Hill Project Digital Hub To Remember the Lost Urban Neighborhood
Who is our President and CEO?
Dr. Mariko Silver
Join us!
It is the policy of Lincoln Center for the Performing Arts, Inc., to ensure equal employment opportunity without discrimination or harassment on the basis of race, creed, color, national origin, sex, age, religion, disability, marital or civil partnership/union status, familial or caregiver status, alienage or citizenship status, sexual orientation, gender identity or expression, pregnancy, military or veteran status, genetic information, predisposition, or carrier status, unemployment status, domestic violence, sexual violence, or stalking victim status, or any other characteristic protected by federal, state, or local law. LCPA is committed to creating a dynamic work environment that values diversity and inclusion, respect and integrity, customer focus, and innovation.
If you require reasonable accommodation in locating open positions, completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to Abigail Worsnip at (212) 875–5324.
Salary Context
This $120K-$150K range is in the lower quartile 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 Lincoln Center, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($135K) sits 36% below the category median. Disclosed range: $120K to $150K.
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.
Lincoln Center AI Hiring
Lincoln Center has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $150K - $150K.
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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