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Overview:
This role will serve as a connective layer across Data Science, Data Engineering, Software Engineering, Product, Operations, and business stakeholders. The Technical Project Manager will bring structure, visibility, and execution discipline to our Data Science Lifecycle—from problem definition and data preparation through model development, deployment, monitoring, and continuous improvement.
The ideal candidate has direct experience managing data science or machine learning projects and is comfortable working in a highly technical, fast-moving environment. This is a tactical, execution-oriented role for someone who enjoys turning complex work into clear plans, coordinating dependencies, removing blockers, and ensuring that projects progress toward measurable business outcomes.
Experience in PropTech, FinTech, real estate technology, financial services, or other data-intensive SaS environments is a plus. Experience with MLOps and production machine learning workflows is also highly desirable. Experienced effectively using AI within their own workflow to accelerate/amplify reach and impact. Experience working with sensitive, confidential, personally identifiable, or financially relevant data is a plus.
Responsibilities: What You’ll Do
- Data Science Project Execution Manage the day-to-day execution of multiple data science and machine learning projects.
- Help translate business problems and strategic priorities into clear project scopes, objectives, milestones, deliverables, and success criteria. Maintain project plans, backlogs, roadmaps, timelines, dependencies, risks, decisions, and action items.
- Coordinate work across data scientists, data engineers, software engineers, ML engineers, product managers, and business stakeholders.
- Facilitate sprint planning, standups, retrospectives, status reviews, demos, and project working sessions.
- Identify blockers early, drive resolution, and escalate decisions when necessary.
- Ensure that project documentation, requirements, assumptions, and decisions are current and accessible.
- Provide concise, regular reporting on progress, risks, dependencies, and expected outcomes.
- MLOps and Production Readiness Help Track requirements for reproducibility, versioning, testing, deployment, monitoring, and documentation.
- Help coordinate model release readiness, production handoffs, and post-deployment follow-up.
- Ensure teams consider data quality, model performance, drift, reliability, security, and support ownership.
- Help establish repeatable processes for model promotion, retraining, monitoring, incident response, and model retirement.
- Process Improvement and Operating Rhythm Help create lightweight, scalable operating processes for the Data Science organization.
- Help develop templates, dashboards, meeting cadences, and documentation standards that improve team effectiveness.
- Help identify recurring delivery issues and recommend improvements to processes, tools, roles, or decision-making.
Qualifications: Required:
- 5+ years of experience in technical project management or related delivery role.
- Demonstrated experience managing data science, machine learning, analytics, or other highly technical projects.
- Experience coordinating cross-functional teams like Data Science, Engineering, & Product functions.
- Ability to understand technical discussions involving data pipelines, experimentation, model evaluation, APIs, deployment, and production support.
- Strong project management skills, including planning, prioritization, dependency management, risk management, and status reporting.
- Excellent written and verbal communication skills.
- Ability to operate effectively with ambiguity and bring structure to complex, evolving work.
- Strong attention to detail and follow-through.
- Experience working in Agile, Scrum, Kanban, or similar delivery environments.
- Hands-on experience on how to use AI in workflows effectively and responsibly.
Preferred:
- Strong understanding of the data science and machine learning lifecycle.
- Experience with MLOps, ML platforms, or production machine learning systems.
- Familiarity with model deployment, model registries, experiment tracking, CI/CD, workflow orchestration, model monitoring, data quality monitoring, and retraining processes.
- Familiarity with responsible AI, model risk management, privacy, explainability, bias, or regulatory considerations.
- Technical background in data science, computer science, engineering, analytics, mathematics, or a related discipline.
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 $94,700.00 - USD $161,300.00 /Yr.
Salary Context
This $94K-$161K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($128K) sits 40% below the category median. Disclosed range: $94K to $161K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
RealPage Inc AI Hiring
RealPage Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Richardson, TX, US. Compensation range: $161K - $161K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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