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About This Role
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.
ABOUT THE ROLE
Welltower’s Data Science Intern is someone who is passionate about turning data into insights. This intern will work alongside the Data Science and Business Insights teams to support the development of analytics solutions, integrate information from various sources, and help evaluate data\-driven opportunities across the business. The role will also provide exposure to AI\-enabled processes and advanced analytics initiatives that support strategic decision\-making.
The Data Science Intern will contribute to projects involving data preparation, exploratory analysis, predictive modeling, and business intelligence. This internship offers an opportunity to gain hands\-on experience applying data science techniques within a dynamic real estate and healthcare environment.
KEY RESPONSIBILITIES
- Support the Business Insights team in advancing Welltower’s data and analytics initiatives.
- Assist in the development and testing of predictive models and analytical solutions.
- Help prepare datasets, engineer features, and support machine learning workflows.
- Conduct exploratory data analysis and data mining using modern analytical techniques.
- Assist with integrating and evaluating internal and external data sources.
- Analyze model outputs and communicate insights that support business decisions.
- Support efforts to improve data quality, governance, and collection processes.
- Process, cleanse, and validate data used in reporting and analytics projects.
- Perform ad\-hoc analyses and present findings to team members in a clear and organized manner.
- Collaborate with cross\-functional teams to understand business challenges and identify opportunities for data\-driven solutions.
OTHER DUTIES
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of this employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.
TRAVEL
Minimal travel to other Welltower office locations is possible.
MINIMUM REQUIREMENTS
- Currently pursuing a PhD degree in Data Science, Statistics, Mathematics, Economics, Computer Science, Engineering, Information Systems, or a related field.
- Demonstrated interest in data science, analytics, machine learning, or artificial intelligence through coursework, projects, research, or prior internships.
- Familiarity with data analysis and statistical concepts.
- Exposure to programming languages and data science tools such as Python, R, SQL, or similar technologies.
- Familiarity with data visualization tools such as Tableau, Power BI, or similar platforms is preferred.
- Basic understanding of machine learning concepts and predictive analytics techniques.
- Strong analytical and problem\-solving skills.
- Excellent verbal and written communication skills.
- Ability to work independently and collaboratively in a team environment.
- Interest in real estate, healthcare, finance, or related industries is a plus.
- Coursework in statistics, machine learning, data analytics, predictive modeling, or related disciplines is preferred.
*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.*
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 1,500\+ 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. We also seek to support physicians in our Outpatient Medical buildings with the critical infrastructure needed to deliver quality care.
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.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Welltower, Inc AI Hiring
Welltower, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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