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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.
About The Role
The ML Platform team powers all machine learning experiences across Realtor.com® from personalization and recommendations to search ranking, pricing models, and generative AI. As a Senior Machine Learning Engineer, you'll take a leading technical role in building the consumer\-facing products and backend services that bring these ML capabilities to millions of users. You'll own the full stack from ML model integration to production APIs, working closely with data scientists, ML engineers, and product teams to ship high\-quality, high\-impact experiences.
Three Reasons To Apply
- Build next\-gen consumer experiences powered by personalization, recommendations, and GenAI for millions of users
- Work at the intersection of consumer product engineering and cutting\-edge ML owning both the user\-facing surface and the ML infrastructure behind it
- Drive the technical direction of a platform that powers every ML\-powered feature across Realtor.com®
What You'll Do
- Serve as a technical lead for key consumer\-facing ML initiatives, owning architecture and design across the full stack — from ML model integration to production APIs and user\-facing features
- Design, build, and expose ML\-powered capabilities through *GraphQL APIs and federated subgraphs*, REST endpoints, and event\-driven services consumed by web and mobile clients
- Design, build, and optimize high\-performance recommender systems and personalization features, ensuring relevance, accuracy, and speed for millions of users
- Build and maintain CI/CD pipelines and MLOps workflows using *Metaflow and* *ArgoCD*, ensuring reliable, traceable model deployments to production
- Develop AI\-native and GenAI\-powered services — including LLM and RAG\-based features — that directly improve the consumer experience
- Own the design and development of data pipelines and the integration of ML models into production web and mobile applications
- Drive API design best practices across the team — versioning, schema contracts, backwards compatibility, and cross\-team API governance
- Drive performance optimizations across services, ensuring low latency and high availability at scale
- Collaborate with cross\-functional teams — product, ML, data engineering, frontend, and analytics — to define clear API contracts and translate requirements into scalable technical solutions
- Mentor and coach engineers on best practices, MLOps principles, GraphQL patterns, and production ML integration
- Contribute to the long\-term technical roadmap, driving innovation and reducing technical debt
What You'll Bring
- 7\+ years of software engineering experience, with specific expertise in building consumer\-facing products powered by ML models
- Strong hands\-on experience with *GraphQL* — including federated GraphQL / schema design, and building subgraphs consumed by web and mobile clients
- Deep experience designing and building *RESTful and event\-driven APIs* at scale, with a strong grasp of API contracts, versioning, and cross\-team API governance
- Hands\-on experience with MLOps tooling — *Metaflow, ArgoCD/Argo Workflows,* *and CI/CD pipelines for ML are required*
- Strong experience with cloud\-native architectures on AWS (Lambda, EC2, S3, EKS, or equivalents) and Kubernetes
- Proficient in designing and implementing data pipelines and integrating machine learning models into production, user\-facing applications
- Proven experience building with LLMs, RAG, and GenAI — shipping these capabilities into consumer products is required, not a plus
- Strong communication and collaboration skills — able to drive technical alignment across ML, data science, product, frontend, and engineering teams
- Experience with recommender systems, personalization, or search ranking is a strong plus
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent professional experience.
How We Work:
We balance creativity and innovation on a foundation of in\-person collaboration. Our employees work three days in our Austin headquarters 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 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 $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.
Realtor.com AI Hiring
Realtor.com has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Austin, TX, US.
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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