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About This Role
Who is Stanley Martin Homes?
At Stanley Martin Homes, we believe your work should have a purpose. With us, it truly does.
Our success starts with our people, and we are proud to foster a culture where every team member is valued and supported. At Stanley Martin Homes, you will work alongside passionate, knowledgeable professionals who are committed to doing the right thing, delivering exceptional homebuyer experiences, and putting homebuyers first.
Stanley Martin Homes is one of the largest 25 homebuilders in the United States and it has been consistently one of the fastest growing. We are proud of the people\-first culture that makes it possible.
If you are ready to build a meaningful career and help families find the place they will call home, we would love to connect with you. Join our team and build a career that you will be proud of.
Explore Opportunities Today
The Principal AI Engineer is a role where the domain is the entire business. The Principal AI Engineer will work directly with teams across Finance, Team and Culture, Operations, Sales, Purchasing, and Land to understand how work gets done, identify where agentic AI can remove manual effort and improve decisions, and build the production systems that make it real. The Principal AI Engineer will go deep into enterprise agentic AI toolchains and coding agents (e.g., Claude Code), including configuration, subagents, tool and connector integration, skill authoring, and evaluation. The Principal AI Engineer will establish the platform, standards, and reference patterns that every future agent builder at Stanley Martin will follow.
Responsibilities and Duties
Agentic AI Platform \& Enablement
- Stand up and own Stanley Martin’s agentic AI platform: enterprise agentic tooling and coding\-agent configuration, API access, MCP (Model Context Protocol) servers and secure connectors to core business systems (ERP, cloud data platform, HR, finance, and collaboration systems), and the secure development environment.
- Operationalize the company’s secure AI adoption framework as the baseline for all agent development.
- Build out the enterprise context layer in partnership with Business Technology: turn institutional knowledge, process documentation, business rules, and system metadata into governed, machine\-readable context (knowledge bases, semantic models, and MCP resources) that agents rely on to reason accurately about how Stanley Martin works.
- Partner with EDAP and business stakeholders to operationalize semantic models, business definitions, and enterprise context required for trusted AI solutions.
- Build and maintain a version\-controlled repository of reusable skills, agent templates, and reference architectures.
- Define the standard patterns, starter kits, and guardrails that let EDAP, DEV, and other teams and users build agentic solutions safely on the platform.
Agent Design \& Delivery
- Design, build, and ship production agentic systems end\-to\-end, from discovery and scoping through architecture, implementation, evaluation, and deployment.
- Deliver the team’s first production agents in partnership with executive sponsors across business functions.
- Implement core agentic patterns: tool and function calling, planner\-executor flows, structured output, multi\-step reasoning, retrieval\-augmented generation, and human\-in\-the\-loop controls.
- Build hybrid solutions where agents reason and decide, then call deterministic automations to execute, partnering with Business Technology on the automation layer.
- Operate across a heterogeneous environment that includes cloud infrastructure, an enterprise data platform, productivity and collaboration systems, Microsoft Dynamics ERP, and 3rd party SaaS platforms (e.g., Salesforce).
Evaluation, Review \& Governance
- Build the evaluation harness every agent must pass before production: test cases, accuracy thresholds, and quality regression testing.
- Establish and run the formal agent review process, covering intake, design and data\-classification review, security review, human\-in\-the\-loop tier assignment, and promotion gates from development to production.
- Implement observability for deployed agents, including audit logging of agent actions, telemetry on usage, errors, latency, and drift, and scheduled post\-deployment reviews.
- Implement cost and value controls, including usage monitoring and model routing.
- Ensure every solution aligns with Stanley Martin’s AI policies, data classification standards, least\-privilege access design, and responsible AI guardrails.
Shareability, Catalog \& Federation
- Build and curate the central agent and skill catalog, so agentic capabilities are discoverable and reusable across the company rather than rebuilt.
- Publish documentation standards and contribute reusable connectors and skill packages.
- Support EDAP (data\-platform AI capabilities), DEV (workflow automation and system integrations), and other teams so they build on the platform under shared standards.
- Lead internal showcases and demos that drive adoption across divisions and functions.
Cross\-Functional Partnership \& Technical Leadership
- Partner directly with business stakeholders to map real workflows, capture institutional knowledge, and identify high\-leverage agentic AI opportunities.
- Help triage incoming ideas between agentic AI and conventional automation, routing deterministic work to the right lane.
- Influence technical direction by setting architecture patterns, reviewing designs, and raising engineering standards.
- Mentor engineers and builders across the company on agentic patterns and best practices as adoption grows.
- Stay current on advancements in agent frameworks, evaluation approaches, and responsible AI governance, and translate them into Stanley Martin’s roadmap.
- Complete all other duties as assigned by manager.
- Represent the company professionally in all internal and external interactions and communications.
- Adhere to safety standards and help promote a safe working environment.
- Adhere to and promote the Mission, Vision, and Values of Stanley Martin Homes
Position Standards
- Experience designing APIs, services, and data pipelines. You think in systems, not scripts.
- Ability to operate with high autonomy. You scope your own work, make architectural decisions, and own outcomes.
- Excellent communication skills, with the ability to explain technical concepts and trade\-offs to non\-technical executives and business partners.
- Willingness to work across the full range of business systems, including the unglamorous ones where the high\-impact problems live.
Position Requirements
- 8\+ years of professional engineering experience, with a track record of designing, shipping, and operating production systems.
- Bachelor's degree in computer science or a related field, or equivalent practical experience.
- Hands\-on experience building with LLM APIs, agent frameworks, or AI\-powered applications that ran in production or were used by real people, including agentic patterns such as tool use, orchestration, and human\-in\-the\-loop design.
- Depth with modern agentic AI toolchains and coding agents including Anthropic Claude, Snowflake, Omni, and MCP\-based architectures, or a demonstrated ability to develop that depth quickly, including connector and tool integration, skill and subagent design, and prompt engineering.
- Cloud fluency, with Microsoft Azure strongly preferred; experience with enterprise cloud data platforms (e.g., Snowflake or comparable).
- Evaluation and testing discipline: you can define what good means for an agent and prove it before production.
- Security\- and governance\-minded: comfortable with data classification, least\-privilege design, auditability, and human\-in\-the\-loop controls.
- Strong proficiency in Python; comfort with additional languages (TypeScript/JavaScript, SQL) is a plus.
- Willingness to work in a hybrid environment (3 days per week onsite at Reston, VA headquarters).
Preferred Qualifications
- Experience building shared AI infrastructure or developer platforms that enable multiple teams to build on your foundation.
- Familiarity with multi\-agent orchestration patterns: task decomposition, tool\-use pipelines, agent memory, and human\-in\-the\-loop workflows.
- Experience with retrieval\-augmented generation, embeddings, vector search, and unstructured\-document pipelines (ingestion, chunking, metadata enrichment).
- Experience with LLMOps/MLOps tooling: model registries, CI/CD, monitoring and observability, drift detection, and cost controls.
- Experience with the Microsoft AI ecosystem (Copilot Studio, Azure AI Foundry, Power Platform) and judgment about when to lean in versus build.
- Experience turning ambiguous business processes into working agents in a non\-tech industry (construction, real estate, manufacturing, financial services).
- Track record of mentoring engineers and influencing senior technical and business leaders.
What’s In It For Me:
- Access to competitively priced, high\-quality health care options through Aetna, MetLife and EyeMed, along with employer\-paid Short Term and Long Term disability, basic life and AD\&D insurance (including employee\-paid life, Legal Resources, and Aflac supplemental options)
- Plan for the future by investing in a 401(K), with up to $5K employer match, invest even more with our Health Savings Account (HSA)
- Put your family first with benefits, including 3 weeks of paid parental leave and a Flexible Spending Account (FSA) for dependent care
- 12 weeks of paid maternity leave through our Short\-Term Disability Plan
- Receive well\-rounded wellness benefits, including free and low\-cost mental health resources and support services through our Employee Assistance Program
- Continue your education with tuition and certification reimbursement
- Rest and relax with 15 days of vacation (increases with tenure) and 6 days of paid sick leave
- Protect yourself from identity theft or travel mishaps with our no\-cost coverage
- Receive great discounts on buying a Stanley Martin home and discounts with our partners in mortgage and title services as well as cell phone service through Verizon
- Get access to your paycheck early with an advanced pay option through Dayforce Wallet
- Support local charities that are important to you through our Giving Back Program; with up to $250 match, 8 hours leave and more
Stanley Martin Homes has been building new homes since 1966\. Headquartered in Reston, VA, Stanley Martin Homes is one of the nation’s fastest\-growing homebuilders, having built more than 25,000 homes and operating in 14 metropolitan areas and seven states, including Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, and West Virginia. Named National Builder of the Year in 2021 by Builder Magazine, Stanley Martin Homes is driven to deliver on its mission to “design and build homes people love at a price they can afford.”
At Stanley Martin Homes, you're not just joining a company—you’re joining a team. Whether your passion is in sales, construction, or operations, you'll contribute to delivering an exceptional home buying experience that helps people realize their dreams.
To hear from our team members about why they love working at Stanley Martin Homes, click here.
This position pays between $178,900\.00 to $198,000\.00 annually.
Salary Context
This $178K-$198K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Stanley Martin Homes, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($188K) sits 12% below the category median. Disclosed range: $178K to $198K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Stanley Martin Homes AI Hiring
Stanley Martin Homes has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Reston, VA, US. Compensation range: $198K - $198K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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