Machine Learning Engineer I

Rocket Companies•Lake Vista 1 - 750 E Highway 121, TX

1d•$112,000 - $239,000•Hybrid

About The Position

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.

Requirements

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.

Responsibilities

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).
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).
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.