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About This Role
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Join the Discovery family, where quality and service are at the heart of everything we do!
This role will be located at one of Discovery Land Company’s Corporate Office locations in Beverly Hills, California.
Discovery Land Company is seeking a full-time, year-round Principal AI Engineer to join the Artificial Intelligence Department.
Job Summary:
Discovery Land Company (DLC) develops and operates private club communities across the U.S. and internationally. DLC's AI team builds the platforms, agents, and workflows that help corporate and club teams work faster with less manual effort.
The Principal AI Engineer is the senior technical leader for DLC's AI program, reporting to the VP of Artificial Intelligence. You will co-own DLC's AI strategy and roadmap with the VP of AI, set the technical direction and engineering standards for all AI solutions, and own the highest-impact AI use cases end to end: scope the business problem with department leaders, design the solution, build it, ship it, and measure the hours saved. Time will be split across technical strategy and standards, product ownership, and hands-on engineering.
Duties & Responsibilities:
- Set Technical Direction and Standards: + Co-own DLC's AI strategy and 1-year and 3-year roadmap with the VP of AI, including platform choices and investment priorities. + Define and own DLC's AI reference architecture across agents, MCP integrations, retrieval, and Microsoft 365 automation. + Set and enforce engineering standards for evaluation, logging, guardrails, security, and code review across all production AI solutions. + Lead technical evaluation and selection of AI platforms, models, and vendors. + Mentor engineers and Department AI Leads, and serve as final technical reviewer on AI solution designs.
- Own Product Delivery: + Partner with departments across DLC to identify and prioritize use cases by hours saved and risk. + Write clear requirements, success metrics, and solution plans for each AI use case. + Manage the backlog and maintain a consistent delivery cadence.
- Drive Adoption: + Deliver training, documentation, and feedback loops for end users at the corporate and club levels. + Track adoption metrics and use them to improve deployed solutions.
- Build and Deploy AI Solutions: + Build agents, skills, and automations on Claude (Claude Enterprise, Claude Code, Claude API, and Claude Agent SDK). + Build and maintain MCP server integrations that connect Claude to business systems (CRM, marketing automation, ERP, and document stores). + Build workflows in n8n and/or Microsoft Power Automate and Copilot Studio, integrated with SharePoint, OneDrive, Teams, and Outlook via Microsoft Graph. + Build retrieval and knowledge base solutions on Microsoft 365 and related tooling. + Implement evaluation, logging, and guardrails for all production AI solutions.
- Co-lead AI Governance and Security: + Follow DLC data classification rules and ensure member, guest, and employee data is handled under the strictest applicable privacy standard (e.g., GDPR, CCPA/CPRA). + Lead technical vendor security reviews for new AI tools and connectors.
- What Success Looks Like: + First 6 months
- Ship 3 or more production AI use cases with documented hours saved.
- Define reusable MCP and Power Automate integration patterns that scale across properties and publish them as DLC standards.
- Track adoption metrics and report them monthly to the VP of AI.
- Publish DLC's AI reference architecture and engineering standards, applied to all new AI builds. Deliver the technical portion of DLC's 1-year and 3-year AI roadmap jointly with the VP of AI. -
Skills & Qualifications:
- 8+ years of combined experience in software engineering and product management, including at least 3 years shipping LLM-based products or internal tools to production.
- Hands-on production experience with Claude and the Anthropic ecosystem: Claude Code, the Claude API (tool use, prompt caching, structured outputs), MCP, and Agent Skills.
- Strong experience with the Microsoft 365 stack: Entra ID, SharePoint, Microsoft Graph API, Power Automate, and Teams.
- Proficiency in Python, TypeScript/JavaScript, and SQL.
- Experience integrating enterprise SaaS platforms via REST APIs and connectors.
- Track record of translating ambiguous business requests into shipped solutions with measurable outcomes.
- Demonstrated ownership of technical strategy, reference architecture, or engineering standards adopted across multiple teams or business units.
- Experience mentoring engineers and leading technical design reviews.
- Clear written communication for executive and non-technical audiences.
- Experience with Oracle CX (Sales Cloud, Eloqua), Sage Intacct, or CData Connect AI preferred.
- Experience with Copilot Studio, Power BI, or Dataverse preferred.
- AWS experience (Bedrock, IAM, Lambda) preferred.
- Experience with RAG, evaluation frameworks, and LLM observability.
- Background in hospitality, real estate, or multi-site operations preferred.
Experience building internal AI training or enablement programs preferred. *
Additional Requirements:
- Flexibility and ability to pivot to new projects and desire to work in fast-paced environment.
- Positive attitude, professional demeanor, and exceptional communication and interpersonal skills to deliver service to members, guests, and team members.
- Ability to work in a team environment.
- Ability to stay calm and focused during the busiest of times.
- Ability to read, write, speak, and understand English; additional languages preferred.
- Must be able to work flexible work hours/schedule including evenings, weekends, and holidays. Long hours may be required due to business demands.
Ability to meet the physical demands of the position including, but not limited to, working indoors and outdoors in all weather conditions, standing, walking, and moving for periods of greater than eight (8) hours, and lifting and carrying items sometimes greater than fifty (50) pounds. *
Benefits
- Competitive Pay
- Medical, Dental, and Vision Benefits
- 401k Contribution
- Paid Time Off and Paid Holidays
- Employee Meals, Referral Incentives, and Recognition Programs
- Holiday Pay
- Professional development and upward mobility opportunities
Work-Family Culture *
Discovery Land Company's good faith estimate of the salary range it reasonably expects to pay for this position upon hire is $250k plus bonus.
About Us
Discovery Land Company is a U.S.-based real estate developer and operator of private residential communities with a world-renowned portfolio of domestic and international properties. The mission of Discovery Land Company is to create one-of-a-kind environments that provide individuals and families the freedom, security, and resources to learn, grow, and play in some of the most beautiful places in the world. For more information about our company, please visit: http://www.discoverylandco.com .
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Discovery Land Company, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Discovery Land Company AI Hiring
Discovery Land Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Beverly Hills, CA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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