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About This Role
Description
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 growing how it uses AI, from internal tools and coding assistants to fleets of autonomous agents. We need a security engineer whose core skill is cybersecurity and whose focus is AI. Recent incidents, like an AI model under test breaking into another company's infrastructure, show the new kinds of risk: agents acting outside their intended scope, models and tools pulled from public hubs, and normal automation becoming an attack path. You'll make sure our systems are built to contain all of that, and that the secure option is also the easy one. You'll work closely with News Corp's global cybersecurity team, bringing AI security depth to Realtor.com while aligning with group-wide standards.
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.
What You'll Do:
Secure the AI platform (AWS and GCP)
- Design and enforce guardrails for AI workloads on AWS (Bedrock, Lambda, IAM, KMS, SCPs, GuardDuty, Security Hub) and GCP (Vertex AI, IAM, VPC Service Controls, Org Policies, Security Command Center, Cloud KMS).
- Define how agents and other non-human accounts are identified and authorized: short-lived, narrowly scoped credentials, delegated user permissions, and a per-agent audit trail, so a misbehaving agent can only do limited damage.
- Build containment for agents: separate read and write permissions, network egress controls, sandboxed execution, approval gates for high-risk actions, and kill switches.
- Design an agent gateway or control plane that keeps agents and their tool calls within approved limits.
- Secure the AI supply chain: where models come from and how they're scanned (including unsafe formats like pickle), vetting of models and datasets from public hubs such as Hugging Face, an approval process for new MCP servers and agent tools, and an inventory of the models, data, and tools each system uses (an AI-BOM).
- Defend against prompt injection, tool poisoning, poisoned retrieval data (RAG poisoning), and sensitive data leakage.
- Put detection and logging in place for agent and model activity, and own the incident response playbooks for AI-specific events.
Review and guide what gets built
- Lead threat modeling for new products, features, and internal solutions, from consumer-facing experiences to internal tools and agent workflows. Use frameworks like STRIDE, the OWASP LLM/GenAI Top 10, MITRE ATLAS, and OWASP ASVS. Turn the findings into clear design requirements and release criteria.
- Review pull requests, architecture designs, and release-readiness for everything from apps to agent fleets, and make concrete suggestions that move work forward instead of blocking it.
Build paved-road frameworks, reference architectures, and secure-by-default templates that rule out whole categories of risk, instead of catching them one PR at a time.
- Build security into CI/CD: secrets scanning, SAST/SCA, GitHub Actions hardening, and policy-as-code in Terraform.
- Build an ongoing red-team and evaluation program for our models, agents, and LLM apps, and test new AI features before launch.
Partner with News Corp cybersecurity
- Work with News Corp's global cybersecurity team to align Realtor.com's AI and cloud security with group-wide standards, policies, and risk frameworks.
- Coordinate on incident response, threat intelligence, and vulnerability disclosure across News Corp businesses.
- Share AI security patterns, threat models, and lessons learned with other News Corp businesses, and bring their insights back to Realtor.com.
Use AI to do security work
- Build agents that help do the security work: triage findings, investigate alerts, audit IAM for least privilege, and open tested, secure-by-default fix PRs.
- Use Claude Code, Devin, and MCP to make the security team faster, and share those patterns with other engineering teams.
Stay ahead of the field
- Track new AI threats, incidents, research, and vendor capabilities as they emerge, and turn them into concrete actions for our platform. That includes new attack techniques, model and agent releases, and changes to OWASP, MITRE ATLAS, and other frameworks.
- When a major industry incident happens, quickly assess whether we're exposed and lead the response.
- Prototype new approaches when existing tools and best practices don't fit yet. Run small experiments, measure the results, and turn what works into platform standards.
- Share what you learn: write short internal briefings on major developments and recommend when to adopt, pilot, or skip a new tool or practice.
Educate and lead
- Create and run AI security training for engineers, product teams, and the wider company: prompt injection, data leakage, excessive agency, and secure use of tools like Claude, Glean, Devin, and Gemini.
- Act as the security voice in AI platform strategy: evaluate vendors and models, set the bar for what's production-ready, and help shape AI governance across the whole AI lifecycle.
- Brief leadership on AI risk and where to invest.
- Represent Realtor.com externally through conference talks, open-source work, or industry working groups.
What You'll Bring:
- 5+ years in cybersecurity, cloud security, AppSec/product security, or security platform engineering.
- Hands-on depth in AWS security, plus real experience in GCP (or a strong multi-cloud background).
- You know that telling an AI not to do something isn't security. You think in permissions, scopes, logs, approval gates, data boundaries, and rollback.
- Experience securing LLM or agentic systems, including prompt injection defenses, retrieval and data-boundary risks, agent identity, and model or tool supply-chain integrity.
- Experience doing threat modeling for products and platforms, not just infrastructure. You can lead a session with engineers and product managers and turn it into requirements they'll actually follow.
- Hands-on experience building with agents, tool calling, or MCP, and with AI coding tools.
Strong code-review skills in at least one of Python, TypeScript, or Go, and comfort with Terraform/IaC.
- Experience with zero trust, least privilege, SSO/OIDC/OAuth, secrets management, and vulnerability management.
- Experience working within a larger security organization, such as a parent company, central security team, or federated model. You can balance local speed with alignment to enterprise standards.
- Follow AI security research, incident write-ups, and the security community, and you can point to something recent you learned and acted on.
- A builder's mindset. You'd rather prototype a new control than wait for a vendor to ship one, and you're comfortable working where the playbook doesn't exist yet.
- Explain risk clearly to any audience, from a PR comment to an executive briefing to a training session for the whole company.
Nice to have:
- AWS Security Specialty, Google Professional Cloud Security Engineer, CCSP, CISSP, OSCP, or GIAC (GCSA/GWEB).
- Experience red-teaming or evaluating AI models or agents.
- Familiarity with NIST AI RMF, ISO 42001, Google SAIF, CoSAI, NIST SSDF, or the EU AI Act.
- Experience securing Snowflake (RBAC, masking, network policy).
- Conference talks, published research, or open-source security contributions.
How We Work:
We balance creativity and innovation on a foundation of in-person collaboration. For most roles, our employees work four 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $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.
Realtor.com AI Hiring
Realtor.com has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Austin, TX, US, Austin, TX.
Location Context
AI roles in Austin pay a median of $209,100 across 306 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 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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