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About This Role
Sr AI Engineer
Location: Austin, TX, United States
Category: Technology
Job ID: 3240
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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.
We are seeking an AI Engineer to join our AI Integrations Team with deep expertise in AI safety, guardrails, and LLM evaluation to join our AI Integrations team. You will be responsible for building and tuning the classifiers that keep our AI assistant compliant, safe, and helpful - working at the intersection of AI and consumer-facing products.
About the Role:
The AI Integrations Team is a cross-functional squad that builds the future of how Realtor.com integrates AI across its products. Our flagship is RealAssist, an LLM-powered real-estate assistant embedded across web and mobile. Because it advises consumers on housing, it operates under real Fair Housing Act compliance obligations - our AI safety layer is a launch gate, not an afterthought. This role owns that layer, including:
- LLM-as-a-judge classifiers for Fair-Housing compliance and content moderation The guardrail evaluation harness that proves it all works before it ships
- A cloud prompt-injection / jailbreak backstop (Google Cloud Model Armor) Technical Ownership
- Build, tune, and own LLM-as-a-judge classifiers for Fair-Housing compliance and content moderation, targeting high recall on disallowed content without overblocking legitimate users
- Design and run guardrail evaluation pipelines: curate labeled datasets, maintain train/test/validate splits, and run offline prod-replay overblocking evals -
- Report confusion matrices and precision/recall/F1 per category to make safety decisions defensible
- Integrate and operate cloud guardrail services as a runtime prompt-injection/jailbreak screening layer, with fail-open behavior and alerting
- Red-team the assistant and wire safety-regression checks into CI so guardrails don't silently degrade as the product grows
- Manage guardrail infrastructure as code with Terraform (templates, IAM, project shape) - Participate in technical design reviews and architecture discussions
- Partner with ML, backend, product, and legal/compliance stakeholders to define risk tiering and get new AI capabilities through safety review before launch
What You'll Bring:
- AI Safety & Evaluation Expertise (Required)
- 5+ years of professional software/ML engineering experience, with hands-on LLM application work
- Bachelor’s degree or equivalent experience
- Strong Python proficiency
- Real classification-metrics literacy - precision/recall/F1 (macro vs weighted), confusion matrix debugging, dataset curation, and calibration to a production distribution -
- Experience building or tuning LLM prompts/classifiers and evaluating them with an eval framework (DeepEval, RAGAS, Arize Phoenix, LangSmith, OpenAI Evals, or a home grown harness)
- Experience turning small seed sets into robust labeled eval datasets (dataset-synthesis tooling, prompt-optimization loops)
- Familiarity with prompt-injection / jailbreak defense concepts (OWASP LLM Top 10, input/output filtering, least-privilege tool access, adversarial testing)
- Guardrails & Infrastructure Experience (Bonus)
- Hands-on with a cloud guardrail service (Google Cloud Model Armor, AWS Bedrock Guardrails, Azure AI Content Safety)
- Terraform / IaC for cloud infrastructure
- Experience with Google Cloud Vertex AI / Gemini
- Understanding of monitoring and observability tools (New Relic or similar)
- Exposure to regulated / compliance-sensitive domains (fair housing, fair lending, healthcare, finance, trust & safety)
- Red-teaming or AI-security background
Expectations
Technical Excellence
- Independently design, implement, and tune guardrail and evaluation systems - Make sound architectural decisions that balance safety, latency, and user experience - Write clean, maintainable, well-tested code that sets the standard for the team - Keep eval datasets calibrated to real traffic and emerging attack patterns - Stay current with AI-safety research, jailbreak techniques, and new guardrail tooling
Impact & Ownership
- Own the AI safety layer end-to-end from design through deployment
- Anticipate problems and proactively address them through red-teaming and regression checks
- Make data-driven decisions grounded in eval metrics
- Deliver high-quality work consistently on schedule
Why Join Us:
Cutting-Edge Technology
- Work with the latest AI technologies from leading providers
- Build the safety layer for LLMs running in production
- Shape the future of safe, compliant AI-powered real estate search at scale - Millions of users rely on Realtor.com to find their next home
- Your work directly affects major life decisions for consumers
- Keep the AI that guides those decisions fair, safe, and trustworthy Growth
- Work alongside Staff and Principal engineers who will challenge and support you - Exposure to full-stack technologies including mobile, backend, and AI - Clear path to career growth with opportunities for technical leadership
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.
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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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
News Corp AI Hiring
News Corp has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Austin, TX, US, New York, NY, US. Compensation range: $130K - $130K.
Location Context
AI roles in Austin pay a median of $208,000 across 317 tracked positions. That's 3% below 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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