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WELLTOWER – REIMAGINE REAL ESTATE WITH US
At Welltower, we’re transforming how the world thinks about senior living and wellness-focused real estate. As a global leader in residential wellness and healthcare infrastructure, we create vibrant, purpose-driven communities where housing, healthcare, and hospitality converge. Our culture is fast-paced, collaborative, and endlessly ambitious—guided by our mantra: The only easy day was yesterday.
We’re looking for bold, independent thinkers who thrive on challenge, embrace complexity, and are driven to deliver long-term value. Every team member is empowered to think like an owner, innovate fearlessly, and lead from where they stand. If you're passionate about outcomes and inspired by the opportunity to shape the future of healthcare infrastructure, we want you on our best-in-class team.
SUMMARY
Welltower is a global leader in wellness-focused and healthcare real estate. We move fast, think like owners, and build for long-term value. This is a hands-on AI engineering role for someone who wants to build from the ground up inside a company with real scale behind it.
The AI Engineer, Product builds the product surface that brings AI capability to the people across the business who rely on it. You own agent-driven tools and interfaces end to end, work directly with users on real data, and iterate fast against live feedback. The emphasis is on the product and the agent behavior behind it, not the front end alone.
KEY RESPONSIBILITIES
- Build and ship the product surface: the agent-driven tools, interfaces, and workflow tooling that teams use to do their work and make decisions
- Design, tune, and evaluate how agents behave in the product so the output is trustworthy enough for users to act on
- Work embedded with users, translating how they actually work into working software on real data
- Get to a live, useful output fast, then iterate against direct feedback
- Build configurable, reusable surfaces rather than one-off rewrites so each build accelerates the next
- Work off a live demo on real data as the spec rather than a lengthy requirements document
MINIMUM REQUIREMENTS
- Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience
- 6+ years shipping production software, with the depth to own a product surface end to end
- Have deployed an agent in a commercial production environment
- Experience building data-dense, decision-support, or analytics products where users act on the output
- Ability to build fast against messy, real data and iterate in front of users
- Based in the NYC metro area and able to work on-site
COMPENSATION
Salaries may vary by location. The base salary range for this role in New York is $160,000 to $220,000. In addition to base pay, this role is eligible for a total rewards package that includes an annual bonus opportunity, equity awards through our long-term incentive program, and a comprehensive benefits suite.
Employment is contingent upon the successful completion of a background check, drug screening, and verification of employment, education, and other credentials relevant to the position.
WHAT WE OFFER
- Competitive Base Salary + Annual Bonus
- Generous Paid Time Off and Holidays
- Employee Stock Purchase Program – purchase shares at a 15% discount
- Employer-matching 401(k) Program + Profit Sharing Program
- Student Debt Program – we’ll contribute up to $10,000 towards your student loans!
- Tuition Assistance Program
- Comprehensive and progressive Medical/Dental/Vision options
- Professional Growth
- And much more! https://welltower.com/newsroom/careers/ (https://welltower.com/newsroom/careers/)
ABOUT WELLTOWER
Welltower® Inc. (NYSE: WELL) (https://welltower.com/) an S&P 500 company, is the world's preeminent residential wellness and healthcare infrastructure company. Our portfolio of 2,500+ Seniors and Wellness Housing communities is positioned at the intersection of housing, healthcare, and hospitality, creating vibrant communities for mature renters and older adults in the United States, United Kingdom, and Canada.
Our real estate portfolio is unmatched, located in highly attractive micro-markets with stunning built environments. Yet, we are an unusual real estate organization as we view ourselves as a product company in a real estate wrapper driven by relationships and unconventional culture.
Through our disciplined approach to capital allocation powered by our data science platform and superior operating results driven by the Welltower Business System, we aspire to deliver long-term compounding of per share growth and returns for our existing investors – our North Star.
Welltower is committed to leveraging the talent of a diverse workforce to create great opportunities for our business and our people. EOE/AA. Minority/Female/Sexual Orientation/Gender Identity/Disability/Vet
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $160K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Welltower, Inc, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $160K to $220K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Welltower, Inc AI Hiring
Welltower, Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $220K - $220K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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