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About This Role
D.R. Horton, Inc., the largest homebuilder in the U.S., was founded in 1978 and is a publicly traded company on the New York Stock Exchange. It is engaged in the construction and sale of high-quality homes designed principally for the entry-level and first time move-up markets. The Company also provides mortgage financing and title services for homebuyers through its mortgage and title subsidiaries. Please visit our website at www.drhorton.com for more information.
D.R. Horton, Inc. is currently looking for a Forward Deployed AI Engineer. As the nation's largest homebuilder by volume, D.R. Horton is transforming how it uses data, AI, and digital solutions for the next generation of homebuilding, and we pair the reach of a national enterprise with a culture built on local ownership, practical innovation, and deep business partnerships. Our teams focus on solutions that work in the field and deliver real results, not technology for its own sake. In this role, you'll have a critical opportunity to shape modern capabilities that will influence how the company operates for years to come.
The Forward Deployed AI Engineer will work at the intersection of business problems and technical execution, helping turn emerging AI capabilities into practical solutions. This role will partner directly with business and technology teams to discover opportunities, rapidly prototype, connect data and AI to real workflows, and help move successful ideas into production. The ideal candidate is equally comfortable in a business conversation and writing code, thrives in ambiguity, and enjoys demonstrating what is possible through working solutions. Success in this role means working solutions in the hands of business users within weeks, measurable improvement in the workflows they touch, and patterns that other teams reuse.
Essential Duties and Responsibilities include the following. Other duties may be assigned.
- Embed with business and field teams to understand workflows, decisions, pain points, data, local variation, and measurable outcomes
- Rapidly prototype and productionize AI-enabled applications, agents, copilots, APIs, workflow automations, and decision-support experiences
- Connect enterprise data, models, APIs, and third-party capabilities into practical end-user solutions
- Work with Product to define MVP scope and with Architecture, Platform, Governance, and Security to ensure solutions can scale safely
- Drive field testing, feedback, adoption, and iteration and translate lessons from one business unit into reusable enterprise patterns
- Represent the function in cross-functional discovery, architecture, governance, operational-readiness, and continuous-improvement activities as appropriate
- Stay current on relevant data, AI, digital, software, and industry practices and apply new capabilities when they create practical business value
- Identify, shape, and pilot new opportunities where data, AI, and digital capabilities can create business value, operating with an owner's mindset and a bias toward building, testing, and learning quickly
- Conduct all business in a professional and ethical manner
- Ability to travel overnight
Qualifications
Education and/or Experience
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience
- 5+ years of software engineering, AI engineering, solutions engineering, data engineering, or comparable experience
- Strong Python and/or TypeScript/JavaScript skills with experience building APIs, applications, integrations, and AI-enabled solutions
- Practical experience with LLMs, RAG, agents, tool and MCP integration, prompt/context engineering, evaluation, document AI and unstructured data, and production AI patterns
- Exceptional communication and relationship-building skills with willingness to spend significant time with business users and field teams
- Demonstrated ability to work independently, take ownership, communicate clearly, and deliver results in an evolving environment
- Ability to build trusted relationships across the organization, balancing enterprise standards with business needs and operating realities
Preferred Qualifications
- Experience in forward-deployed engineering, consulting, solution architecture, product engineering, or startup environments, and experience working across multiple model providers and AI platforms, is preferred
- Experience in homebuilding, real estate, construction, mortgage, title, land development, sales, purchasing, finance, customer care, or another distributed operating business is a plus
- Experience working in a large enterprise with complex enterprise environments is preferred
- Experience founding, incubating, or building new products, capabilities, or businesses from the ground up is a plus
Come join a winning team with a Fortune 500 company! We are growing fast and are looking for enthusiastic attitudes and team players to join our success. We offer an excellent benefits package including:
- Medical, Dental and Vision
- 401(K)
- Employee Stock Purchase Plan
- Flex Spending Accounts
- Life & Disability Insurance
- Vacation, Sick, Personal Time and Company Holidays
- Multiple Voluntary and Company provided Benefits
Build YOUR future with D.R. Horton, America’s Builder.*#WeBuildPeopleToo*
Job
: ExecutivePrimary Location
: TX-ArlingtonOrganization
: CorporateSchedule
: Full-timeJob Posting
: Oct 20, 2026, 10:31:00 AM
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At D.R. Horton, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
D.R. Horton AI Hiring
D.R. Horton has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Arlington, TX, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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