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About This Role
### Join an award\-winning company!
Who are we?
Since 1967, Toll Brothers has been building luxury homes and communities in the best locations in the U.S. Today, we’re a Fortune 500 company operating in over 60 markets. We’re the country’s premier luxury builder with the widest range of product offerings and price points in the industry, serving first\-time, move\-up, active\-adult, and second\-home buyers.
From the homes we build to the talent we recruit, we know that to be the best, we have to work with the best. Toll Brothers is a place where diverse perspectives and experiences are welcomed and where employees of all backgrounds are treated with fairness, dignity and respect. We believe every employee should feel safe to be their true and authentic self at work. Our employees are our family, and we strive to uphold the values that our founders instilled in us, creating an exceptional place to work that is inclusive to all.
Toll Brothers, America's leading luxury home builder, seeks a Director, Applied AI to join our team at our Corporate office in Fort Washington, Pennsylvania.
What is the opportunity?
The Director, Applied AI reports to the SVP, Technology \& Innovation and is a senior leader on the Technology \& Innovation team. This role leads the practical application of AI, automation, analytics, and adjacent technologies across Toll Brothers, translating business problems into production\-ready capabilities that improve speed, accuracy, customer experience, and employee effectiveness. The team builds systems that automate routine work, surface better information at decision points, and make it easier for employees to act with confidence without replacing their judgment or expertise.
This is a strategy, delivery, and people leadership role. The Director partners with leaders across Sales, Production, Purchasing, Marketing, Finance, Customer Experience, and IT to identify high\-value use cases, set priorities, and scale solutions through day\-to\-day operating workflows. The role also establishes reusable platforms, standards, measurement practices, and feedback loops so successful pilots become durable enterprise capabilities rather than isolated tools.
What are the primary responsibilities?
- Set and drive the applied AI roadmap, including automation priorities, in alignment with business priorities and enterprise technology strategy
- Lead the delivery of production AI systems, workflow automation, predictive models, and decision\-support tools used across the business
- Build reusable AI\-enabled automation capabilities that teams can adopt across functions without rebuilding similar solutions independently
- Partner with leaders across Sales, Production, Purchasing, Marketing, Finance, Customer Experience, and IT to identify, prioritize, and deliver high\-value use cases
- Establish feedback loops that capture employee input, model performance, adoption patterns, and business outcomes to improve solutions over time
- Enable employees to automate routine work through training, tooling, standards, and hands\-on support
- Establish operating rhythms for portfolio prioritization, delivery governance, risk management, adoption, and performance measurement
- Recruit, develop, and lead a multidisciplinary team of engineers, architects, automation specialists, and program leaders
- Evaluate platforms, vendors, and emerging technologies, recommending where to build internally and where to adopt external solutions
- Define standards for security, data privacy, responsible AI, integration quality, and production readiness across the team’s work
- Communicate progress, risks, trade\-offs, and recommendations clearly to executive stakeholders and cross\-functional partners
- All other duties as assigned
This is an excellent opportunity to join one of the nation's most respected Fortune 500 companies!
Qualifications
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Does this describe you?
- Results\-driven leader who sets clear priorities, measures progress, and follows through on commitments
- Strong communicator who builds trust across technical teams, business leaders, and executive stakeholders
- Pragmatic problem solver who connects business needs to practical technology solutions
- Curious technologist who tracks where AI and automation are heading and turns new capabilities into practical value
- Thoughtful people leader who hires well, develops talent, and creates accountability across the team
- Adaptable leader who can balance long\-term direction with near\-term delivery
- Sound decision maker who uses evidence, exercises good judgment, and adjusts quickly when facts change
- Customer\-minded leader who considers how employees, partners, and homeowners experience the solutions being delivered
Do you have these qualifications?
- Essential:
+ Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, Business, or a related field
+ 10\+ years of experience in technology, data, automation, digital product, enterprise systems, or related disciplines
+ 5\+ years of experience leading teams responsible for delivering production technology capabilities in complex enterprise environments
+ Experience leading enterprise AI, automation, data, or digital product initiatives from concept through production with measurable business impact
+ Depth in modern AI capabilities, automation platforms, analytics tools, systems integration, and data\-driven product practices applied to business problems
+ Strong record of leading cross\-functional programs that require alignment across business leaders, technical teams, and external partners
+ Experience establishing governance, adoption, measurement, and risk management practices for AI, automation, or data\-driven systems
- Preferred:
+ Master’s degree, MBA, or other advanced degree in a relevant field
+ Experience in homebuilding, real estate, production operations, field operations, or another operationally complex industry
+ Familiarity with platforms such as Salesforce, Snowflake, Power BI, JD Edwards, Power Automate, Workfront, or Jira
+ Experience evaluating vendors, piloting new technologies, and scaling successful solutions in an enterprise setting
+ Experience with AI\-enabled workflow design, decision\-support systems, customer\-facing digital tools, or operational analytics
We offer an excellent compensation and benefits package that includes comprehensive medical/dental, 401(k) with a company match, discounted stock purchase, discounts on mortgages, homes, and much more!
Come see why Toll Brothers has been attracting and retaining some of the best professionals in the industry!
APPLY ONLINE TODAY!
Toll Brothers is committed to ensuring equal employment opportunity. All employment decisions, policies, and practices are in accordance with applicable federal, state, and local anti\-discrimination laws. Toll Brothers will not engage in or tolerate unlawful discrimination (including any form of unlawful harassment) on account of a person's sex (including pregnancy), age, race, color, religion, national origin, ancestry, citizenship, physical or mental disability, sexual orientation, gender nonconformity, status as a transgender individual, gender identity, genetic information, marital status, family responsibility, armed services, or any other status protected by law.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Toll Brothers, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Toll Brothers AI Hiring
Toll Brothers has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Washington, PA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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