Interested in this AI/ML Engineer role at RealPage Inc?
Apply Now →Skills & Technologies
About This Role
Overview:
We are looking for a Staff Engineer to own and evolve our financial data integration and ML classification platform. This system ingests hundreds of variations of real estate financial reports — trial balances, rent rolls, budgets, forecasts, aged receivables — from property management companies, automatically detects their format, classifies their structure using ML models, and transforms them into normalized data for downstream analytics.
You will be the primary technical lead across two critical systems: a document parsing engine handling 280\+ specialized ETL processors and a dynamic pipeline orchestration platform that uses ML\-predicted field mappings to automate data extraction at scale. This role demands breadth — you will build ML models, maintain production data pipelines, design database schemas, and make architectural decisions independently. You will also mentor and manage one direct report.
Responsibilities:
- ML Model Development \& Deployment — Build, train, and deploy classification models (currently served via Wallaroo) that predict financial field/table mappings from document headers. Extend existing BERT\-based question\-answering models used for extracting structured data from free\-text property descriptions.
- Pipeline Platform — Maintain and extend the configuration\-driven orchestration system that matches incoming files to processing pipelines, executes dynamic conditionals, and writes standardized output to downstream templates.
- ETL Engine — Evolve the parsing library that handles complex Excel workbooks with nested headers, multi\-tab structures, merged cells, and varied accounting system formats (ARES, GIC, DCS).
- Data Quality \& Reliability — Improve prediction accuracy, expand audit logging, and ensure processing integrity across clients and document types.
- Architecture \& Technical Leadership — Drive technical decisions on model serving infrastructure, database schema design, and API integration patterns with RealPage's Data Management Gateway (DMG). Provide mentorship and technical guidance to your direct report.
Qualifications:
- 5\+ years Python development with production ML systems
- Strong experience with NLP / text classification — specifically training and fine\-tuning transformer models (Hugging Face, TensorFlow) for document understanding tasks
- Deep proficiency with pandas, NumPy, SQLAlchemy, and PostgreSQL
- Experience building and maintaining ETL pipelines that process messy, semi\-structured data (Excel, CSV) at scale
- Familiarity with ML model serving platforms (Wallaroo, SageMaker, Vertex AI, or similar) including OAuth2\-based inference APIs
- Comfort operating as a technical lead — triaging bugs, shipping features, making architectural calls independently, and mentoring junior engineers
- Demonstrated ability to work across the full stack of a data platform: from raw file ingestion through model inference to API integration
Preferred
- Domain experience in real estate finance, property management, or accounting data (chart of accounts, trial balances, rent rolls)
- Experience with SFTP\-based file processing workflows and Paramiko
- Familiarity with dynamic code generation / evaluation patterns for configurable data transformations
- Background in document parsing / OCR / intelligent document processing
- Experience with SSH\-tunneled database connections and multi\-environment deployments
- Prior experience mentoring or leading a small team
Tech Stack
- Python
- Pandas
- TensorFlow
- Hugging Face Transformers
- PostgreSQL
- SQLAlchemy
- Wallaroo ·
- Paramiko/SFTP
- openpyxl/xlrd
- REST APIs
- OAuth2/JWT
Why This Role
You will have high autonomy over a system that directly impacts how financial data flows through RealPage's platform. The ML models you build will reduce manual data mapping for enterprise property management clients, and the pipelines you maintain process real financial documents daily. This is not a research role — it is applied ML engineering where your models ship to production and your code runs against real client data. As a Staff Engineer, you will shape the technical direction of the platform and have meaningful influence over how the team grows.
*SALARY AND BENEFITS** *RealPage provides a competitive salary package along with a comprehensive benefit plan that includes:*
- *Health, dental, and vision insurance.*
- *Retirement savings plan with company match.*
- *Paid time off and holidays.*
- *Professional development opportunities.*
- *Performance\-based bonus based on position.*
*Compensation may vary depending on your location, qualifications including job\-related education, training, experience, licensure, and certification, that could result at a level outside of these ranges. Certain roles are eligible for additional rewards, including annual bonus, and sales incentives depending on the terms of the applicable plan and role as well as individual performance.* *Equal Opportunity Employer: RealPage Company is an equal opportunity employer and committed to creating an inclusive environment for all employees.*
Pay Range: USD $107,200\.00 \- USD $182,600\.00 /Yr.
Salary Context
This $107K-$182K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 RealPage Inc, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($144K) sits 20% below the category median. Disclosed range: $107K to $182K.
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.
RealPage Inc AI Hiring
RealPage Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Richardson, TX, US. Compensation range: $145K - $182K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.