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About This Role
Recognized as the No. 1 site trusted by real estate professionals, Realtor.com® has been at the forefront of online real estate for over 25 years, connecting buyers, sellers, and renters with trusted insights and expert guidance to find their perfect home. Through its robust suite of tools, Realtor.com® not only makes a significant impact on the real estate industry at large, but for consumers, navigating the biggest purchase they will make in their life, by providing a user experience that is easy to use, easy to understand, and most of all, easy to make decisions.
Join us on our mission to empower more people to find their way home by breaking barriers to entry, making the right connections, and building confidence through expert guidance.
Realtor.com is looking for an AI Platform Engineer to build, operate, and continuously improve the internal AI tooling foundation that powers safe, scalable AI adoption across the company. This role will focus on access controls, platform maintenance, operational support, and governance for key AI tools including Portkey, Claude, Gemini, Windsurf, and Glean.
You will partner closely with AI Solutions Engineers, Security, IT, Legal, and business teams to ensure our approved AI surfaces are reliable, well\-governed, cost\-aware, and easy to use. This role is intended to handle infrastructure, gateway operations, and production\-grade reliability, freeing solution builders to focus on workflow design and delivery.
If you thrive on making complex platforms secure, maintainable, and operationally excellent—and enjoy helping users successfully adopt new tooling—this role is for you.
Top reasons to apply
- Help shape the enterprise AI productivity platform and the paved\-path tooling stack at Realtor.com, including Glean, Portkey, Claude, Gemini, Windsurf, and related platforms.
- Build the operational backbone for AI by improving governance, access, support, observability, and reliability across our approved tools.
- Make a company\-wide impact by enabling safe AI adoption for employees while reducing fragmentation, vendor sprawl, and uncontrolled spend.
What you'll do
- Own day\-to\-day platform operations for Portkey, Claude, Gemini, Windsurf, and Glean, including provisioning, deprovisioning, access requests, license hygiene, and support workflows.
- Design and enforce access controls such as SSO, role\-based access, least privilege, connector approvals, and periodic access reviews across AI tools and integrations.
- Operate and mature the LLM gateway layer with Portkey or similar tooling to centralize model access, routing, observability, guardrails, spend limits, rate limiting, and PII controls.
- Support approved AI platforms as part of the company's paved path, helping define where each tool fits and how teams should use them responsibly.
- Monitor platform health, usage trends, reliability, incidents, and cost signals; turn findings into operational improvements, support documentation, and decision\-ready reporting.
- Lead maintenance activities such as configuration updates, version management, vendor coordination, renewal support, and change\-management readiness for AI platforms and integrations.
- Create and maintain runbooks, support guides, operating procedures, and handoff documentation so teams can use AI tooling confidently and sustainably.
- Partner with Security, Legal, and governance stakeholders to align tooling with approved policies, acceptable use rules, data handling requirements, and News Corp expectations.
- Help standardize and rationalize the AI tool stack, including migrations away from legacy or duplicative tools and toward governed enterprise\-supported platforms.
- Serve as an escalation point for user issues and operational blockers, working across internal teams and vendors to resolve problems quickly and improve the user experience over time.
What you'll bring
- 5\+ years experience administering or supporting enterprise AI tools, developer tooling, or SaaS platforms with a strong focus on access management, operational support, and maintenance.
- Hands\-on familiarity with tools such as Glean, Gemini, Windsurf, Claude, and Portkey, or closely related AI platforms.
- Strong understanding of SSO, RBAC, least privilege, identity lifecycle management, and secure connector governance.
- Experience with observability, monitoring, incident alerting, and service reliability practices that support SLAs and platform health.
- Comfort working with APIs, cloud platforms, and technical integrations across enterprise systems.
- Familiarity with change management, documentation, configuration management, and support processes for production systems.
- Strong judgment around AI governance, data handling, and safe rollout practices in enterprise environments.
- A service\-oriented mindset with the ability to translate technical constraints into practical guidance for both technical and non\-technical users.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field—or equivalent practical experience.
How success is measured in year 1
- Core AI tools have clear, scalable access and support processes with strong license hygiene, documented ownership, and timely provisioning and deprovisioning.
- Portkey or the approved gateway layer is operating as a trusted foundation for centralized model access, guardrails, and observability.
- Claude, Gemini, Windsurf, and Glean are supported through a more governed, maintainable, and cost\-aware operating model.
- AI Solutions Engineers and business teams can rely on stable paved\-path platforms, runbooks, and support channels rather than ad hoc platform ownership.
- Leadership has better visibility into tool adoption, usage, costs, risks, and reliability through recurring reporting and operational metrics.
How We Work
We balance creativity and innovation on a foundation of in\-person collaboration. For most roles, our employees work three or more days in our offices, where they have the opportunity to collaborate in\-person, adding richness to our culture and knitting us closer together.
How We Reward You
Realtor.com is committed to investing in the health and wellbeing of our employees and their families. Our benefits programs include, but are not limited to:
- Inclusive and Competitive medical, Rx, dental, and vision coverage
- Family forming benefits
- 13 Paid Holidays
- Flexible Time Off
- 8 hours of paid Volunteer Time off
- Immediate eligibility into Company 401(k) plan with 3\.5% company match
- Tuition Reimbursement program for degreed and non\-degreed programs
- 1:1 personalized Financial Planning Sessions
- Student Debt Retirement Savings Match program
- Free snacks and refreshments in each office location
Do the best work of your life at Realtor.com®
Here, you'll partner with a diverse team of experts as you use leading\-edge tech to empower everyone to meet a crucial goal: finding their way home. And you'll find your way home too. At Realtor.com®, you'll bring your full self to work as you innovate with speed, serve our consumers, and champion your teammates. In return, we'll provide you with a warm, welcoming, and inclusive culture; intellectual challenges; and the development opportunities you need to grow.
Diversity is important to us, therefore, Realtor.com® is an Equal Opportunity Employer regardless of age, color, national origin, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, marital status, status as a disabled veteran and/or veteran of the Vietnam Era or any other characteristic protected by federal, state or local law. In addition, Realtor.com® will provide reasonable accommodations for otherwise qualified disabled individuals.
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 Realtor.com, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Realtor.com AI Hiring
Realtor.com has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Austin, TX, 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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