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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 a Senior AI Platform Engineer to architect, build, and continuously improve the internal AI tooling foundation that powers safe, scalable AI adoption across the company. This role blends software engineering rigor with core enterprise platform management. You will be responsible for building the software frameworks, protocols, and developer harnesses that allow autonomous agents to interact safely, while simultaneously owning the operational health, access governance, maintenance, and license hygiene of our key AI tools (Portkey, Claude, Gemini, Devin, and Glean).
We are looking for a rigorous practitioner who advocates for software engineering excellence across the full Product Development Life Cycle (PDLC). You will leverage AI through spec\-driven design, automation\-driven provisioning, and robust platform maintenance, eliminating ad hoc ownership and freeing solution builders to focus on workflow design and delivery.
Top Reasons to Apply:
- Architect and Operate the AI Paved Path: Shape the enterprise AI productivity platform and the paved\-path stack at Realtor.com, combining core platform engineering with daily operational excellence.
- Build the Operational Backbone for AI: Drive the programmatic adoption of emerging open protocols like the Model Context Protocol (MCP) while significantly maturing platform governance, access, and observability.
- Make a Company\-Wide Impact: Enable safe AI adoption for all employees while systematically reducing stack fragmentation, vendor sprawl, and uncontrolled spend.
What You'll Do:
- Design \& Implement Agentic Architecture: Build the core software platform, APIs, and SDKs that enable agentic use cases, focusing on orchestration, memory layers, and state management.
- Own Day\-to\-Day Platform Operations: Manage the operational lifecycles for Portkey, Claude, Gemini, Devin, and Glean, including provisioning, deprovisioning, access requests, license hygiene, and technical support workflows.
- Standardize AI Protocols \& Connectors: Implement and extend emerging industry protocols and standards, including Model Context Protocol (MCP), Agent\-to\-Agent (A2A) communication, and Agentic Resource Definition (ARD).
- Engineer the LLM Gateway \& Access Layer: Mature our LLM gateway layer (leveraging Portkey or custom infrastructure) to centralize model access, routing, semantic caching, deterministic guardrails, spend limits, rate limiting, and PII controls.
- Enforce Platform Governance via Code: Implement identity lifecycle management, SSO, role\-based access control (RBAC), least privilege, and connector approvals directly into the platform fabric as code (Policy\-as\-Code).
- Lead Maintenance \& Runbook Engineering: Lead platform maintenance activities including configuration updates, version management, vendor coordination, and renewal support. Create and maintain robust runbooks, support guides, and handoff documentation.
- Serve as an Operational Escalation Point: Partner closely with Security, IT, Legal, and business teams to resolve user issues and operational blockers, aligning tooling with acceptable use policies and data handling rules.
- Build the AI platform. Help create a greenfield AI platform and the lifecycle around it so teams can use AI in a safe and consistent way. Define the standards and guardrails for how AI systems are built, governed, deployed, monitored, rolled back, and shut down when needed.
- Strengthen AWS infrastructure. Design, secure, and scale AWS infrastructure for AI systems and platforms. Bring strong experience with Bedrock, IAM, security, network security, private connectivity, egress controls, and policy\-based access to services and tools.
- Drive Architectural Alignment via Technical Reviews: Facilitate collaborative design reviews to define and socialize AI architectural standards across the organization. Use this process to codify best practices and ensure that "paved path" solutions are practical, vetted, and widely adopted by engineering teams.
- Lead Cross\-Departmental Technical Partnerships: Act as a central technical stakeholder, proactively partnering with engineering departments to validate requirements, unify solution architectures, and ensure platform standards align with real\-world engineering needs, effectively reducing fragmentation and "Shadow AI."
What You'll Bring:
- 5\+ years of Software, Platform, or Systems Administration experience: A proven track record administering or supporting enterprise AI tools, developer tooling, or SaaS platforms with a strong focus on access management, support, and maintenance.
- Hands\-on Familiarity with the AI Stack: Experience configuring, deploying, or building systems that leverage agentic architectures, tool\-calling, and platforms like Glean, Gemini, Devin, Claude, and Portkey.
- Identity \& Governance Expertise: Strong understanding of SSO, RBAC, least privilege, identity lifecycle management, secure data handling, and secure connector governance in an enterprise environment.
- Solid Engineering \& Infrastructure Fundamentals: Proficiency in modern backend languages (Python, Go, TypeScript) and technical integrations alongside observability, monitoring, incident alerting, and service reliability practices.
- PDLC Rigor: A strong engineering philosophy rooted in documentation, change management, configuration management, and support processes for production\-grade systems.
- Service\-Oriented Mindset: The ability to translate technical constraints into practical guidance for both technical and non\-technical users, helping teams adopt new tools safely and sustainably.
How Success is Measured in Year 1:
- Governed \& Scalable AI Paved Path: Core AI platforms have clear, scalable access and support processes with strong license hygiene, documented ownership, and automated provisioning flows.
- Robust Gateway Foundation: Portkey or the approved gateway layer is operating as a trusted, highly available foundation for centralized model access, guardrails, and real\-time observability.
- Operational Rigor \& Support: Automated evaluation and testing frameworks are introduced, and solution teams can rely on stable paved\-path platforms, documented runbooks, and active technical support channels.
- Stack Rationalization \& Visibility: Leadership has precise visibility into tool adoption, usage trends, costs, risks, and reliability through recurring reporting and centralized 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At News Corp, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
News Corp AI Hiring
News Corp has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager. Positions span New York, NY, US, Austin, TX, US. Compensation range: $95K - $270K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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