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About This Role
*Hybrid role \- Work in office Tuesday and Wednesday at Lewisville location. Must be local to DFW, TX to be considered.*
This posting will be open untilAugust 19, 2026\. Applications received after this date may not be considered.
Xome's Data Science team builds the models and data infrastructure behind real estate valuation, auction pricing, investor matching, and mortgage servicing analytics. We're looking for an early\-career Machine Learning Engineer / Data Engineer I to help build and support the data pipelines and ML systems that power these products, working primarily in Python and Azure.
This role is ideal for someone who enjoys working with data, building reliable pipelines, deploying machine learning models to production, and learning modern cloud and MLOps tooling. You'll work closely with Data Scientists, ML Engineers, and Data Engineering to take models from prototype to production and keep the data feeding them clean and trustworthy.
About therole
Data Engineering
- Design, build, and maintain Python\-based data pipelines for ingestion, transformation, and integration across internal systems (e.g., SharePoint via Microsoft Graph API) and external sources.
- Build and support ETL/ELT processes feeding the team's Azure data environment (Azure Data Factory, Azure Blob Storage, Synapse/Fabric).
- Ensure data quality, reliability, and governance across datasets used for modeling and reporting.
- Create and optimize data models, SQL queries, and workflows following the team's standard project structure (raw processed train/test data).
Machine Learning Engineering
- Assist in deploying, monitoring, and maintaining machine learning models in production (e.g., gradient\-boosted ensembles, classification and ranking models).
- Develop data preparation and feature engineering pipelines in Python.
- Support model evaluation, testing, and ongoing performance monitoring, including basic fairness/quality checks where relevant to regulated models.
- Collaborate with Data Scientists to operationalize models and turn research code into production\-ready services.
- Help automate model training, scoring, and deployment workflows, including containerized deployment (Docker/AKS).
Collaboration \& Delivery
- Work with Data Science, Data Engineering, and business stakeholders to understand requirements and translate them into technical solutions.
- Participate in code reviews, testing, and deployment activities using Git\-based version control.
- Document technical solutions, data dictionaries, and operational procedures clearly.
- Continuously learn and adopt new AI, machine learning, and data engineering tools relevant to the team's Azure\-centric stack.
- Support the team's evolving Azure data platform, including the transition toward Microsoft Fabric and Purview\-based governance.
Aboutyou
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
- 0–3 years of professional experience in Data Engineering, Machine Learning Engineering, Software Engineering, or a related technical field.
Whatyou’llget
Our team members fuel our strategy, innovation and growth, so we ensure the health and well\-being of not just you, but your family, too! We go above and beyond to give you the support you need on an individual level and offer all sorts of ways to help you live your best life. We are proud to offer eligible team members perks and health benefits that will help you have peace of mind. Simply put: We’ve got your back. Check out our full list of Benefits and Perks.
Aboutus
Xome® is a technology\-driven real estate company and a subsidiary of Rocket, headquartered in Lewisville, Texas. As a leading innovator in the industry, we use technology to transform the way people buy and sell homes. When you join Xome, you join a team that values curiosity, ownership, and bold thinking. You will take on meaningful challenges, learn from experts across real estate and technology, and have real opportunities to grow. We are insistently different in how we look at the world and committed to an inclusive workplace where every voice is heard. Apply today to grow your career, enjoy amazing benefits, and work alongside leading industry professionals.
*This job description is an outline of the primary responsibilities of this position and may bemodifiedat the discretion of thecompany at any time. Decisions related to employment are not based on race, color, religion, national origin, sex, physical or mental disability, sexual orientation, gender identity or expression, age, military or veteran status or any other characteristic protected by state or federal law. Thecompany provides reasonableaccommodationsto qualified individuals with disabilitiesin accordance withapplicable state and federal laws. Applicantsrequiringreasonable accommodations in completing the application and/orparticipatingin the application process should contact a member of the Human Resources team, at*[email protected]*.*
*The compensation information below is provided in compliance with all applicable job posting disclosure requirements. The compensation for this position is$112,000\.00\-$239,000\.00.The position may also be eligible for an annual bonus, incentives, and other employment\-related benefits including, but not limited to, medical, dental, and vision benefits, 401K retirement plan, and paid\-time off. More informationregardingthese benefits and others can be found*here*. The informationregardingcompensation and other benefits included in this paragraph is the company’s current, good faithestimateat the time of posting. \[Compensation and benefits are subject to modification from time to time as the Company, in its sole and exclusive discretion,deemsappropriate.] The Company maydetermineduring its future reviews of the proposed compensation and benefits provided for this position, that the compensation and benefits for suchpositionshould be reduced. In no event will the Company reduce the compensation for the position to a level below the applicable jurisdictional minimum wage rate for the position. Los Angeles County and San Francisco Candidates only: qualified applicants with arrest or conviction records will be considered for employment per the Fair Chance Ordinance and the Fair Chance Initiative for Hiring.*
Salary Context
This $112K-$239K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rocket, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($175K) sits 18% below the category median. Disclosed range: $112K to $239K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Rocket AI Hiring
Rocket has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lewisville, TX, US. Compensation range: $239K - $239K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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