HO SR. Machine Learning Engineer, Enterprise AI Systems Homedepot · Remote · US · Machine Learning Engineering $100,000–$180,000 6d ago

Aimlroles

Northern (KY)

Hybrid

USD 100,000 - 180,000

Full time

9 days ago

Application generator

An application made for this job — a tailored resume and cover letter that speak straight to the posting.

Get past ATS filters

Job summary

The Home Depot seeks a Sr Machine Learning Engineer to join a product team, designing and embedding ML algorithms into software products. Expect heavy collaboration, production readiness, and ongoing optimization across data, code, and systems. Emphasis on scalable ML, agentic AI, and enterprise data capabilities.

You will work with stakeholders across engineering and product teams, contributing to performance tuning, monitoring, and educational materials to improve adoption and reliability.

Qualifications

5+ years of experience in ML/AI engineering or related field.
Experience designing and developing Agentic AI applications, LLM-powered solutions, retrieval-augmented generation (RAG) systems, and intelligent automation workflows.
Strong experience with knowledge graphs, graph engineering, network analysis, semantic search, and building enterprise knowledge layers that connect structured and unstructured data to enable AI and analytics use cases.
Experience developing scalable data pipelines, data products, and feedback loop architectures that support continuous model and agent improvement.
Proficiency in Python and modern AI/ML frameworks and libraries such as PyTorch, TensorFlow, Scikit-learn, Pandas, and related technologies.
Experience with cloud-native AI/ML platforms and infrastructure, preferably Google Cloud Platform (Vertex AI, BigQuery, BigQuery ML), including model deployment, monitoring, and MLOps practices.
Experience building and supporting AI infrastructure, including vector databases, model serving platforms, APIs, microservices, distributed systems, and high-availability architectures.
Strong understanding of software engineering best practices, including CI/CD, version control, automated testing, security, and performance optimization.
Experience working with large-scale structured and unstructured datasets, SQL, NoSQL, and modern data architecture patterns.
Strong communication, collaboration, and stakeholder management skills with the ability to influence technical decisions across engineering, data, analytics, and product teams.
Demonstrated ability to thrive in ambiguous environments, rapidly learn emerging technologies, solve complex problems, and drive innovation in a fast-paced organization.

Responsibilities

Deliver secure, scalable ML solutions in collaboration with UX, engineering, and product management; ensure quality and change control standards; develop automation for infrastructure, monitoring, and testing.
Participate in learning activities around modern software design, machine learning, and development practices; stay updated with new technologies.
Provide application support for software in production; monitor production SLAs; review performance and capacity across code, data, and prediction quality.

Skills

Machine Learning

AI Engineering

Software Engineering

Agentic AI

LLM-powered

RAG systems

Knowledge graphs

Data pipelines

Python

PyTorch

TensorFlow

Scikit-learn

Pandas

Vertex AI

BigQuery

BigQuery ML

APIs

Microservices

Education

High school diploma or GED

Tools

Python

PyTorch

TensorFlow

Scikit-learn

Pandas

Vertex AI

BigQuery

BigQuery ML

APIs

Microservices

Job description

With a career at The Home Depot, you can be yourself and also be part of something bigger.

Position Purpose:

The Sr Machine Learning Engineer is responsible for joining a product team and contributing to the software design, algorithm design, and overall product lifecycle for a product that our users love. The engineering process is highly collaborative. Sr ML Engineers are expected to pair daily as they work through user stories and support products as they evolve.
ML Engineers may be involved in designing and implementing AI/ML algorithms to embed directly into software products. Activities may include using specific HD process techniques, integration, design, and development. The role could interface with Business Stakeholders, Technology Infrastructure teams, and Development teams to ensure that business requirements are properly met within a machine learning solution. The role may also be involved in performance tuning, testing, and product monitoring. Other responsibilities may include performing customer outreach, designing ML educational material, and data engineering.
Sr ML Engineers should be able to operate independently though will typically work as part of a team with varying skill levels to create, support, and deploy production applications. This role will review submitted code and provide feedback to improve, based on best practices.

Key Responsibilities:

70% Delivery and Execution - Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions; Documents, reviews, and ensures that all quality and change control standards are met; Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable; Writes custom code or scripts to automate infrastructure, monitoring services, and test cases; Writes custom code or scripts to do "destructive testing" to ensure adequate resiliency in production; Configures commercial off the shelf solutions to align with evolving business needs; Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively
10% Learning - Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice); Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations
20% Support and Enablement - Fields questions from other product teams or support teams; Monitors tools and participates in conversations to encourage collaboration across product teams; Provides application support for software running in production; Proactively monitors production Service Level Objectives for products; Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality

Direct Manager/Direct Reports:

This Position typically reports to Software Engineer Manager or Sr. Software Engineer Manager
This Position has 0 Direct Reports

Travel Requirements:

Typically requires overnight travel 5% to 20% of the time.

Physical Requirements:

Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.

Working Conditions:

Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.

Minimum Qualifications:

Must be eighteen years of age or older.
Must be legally permitted to work in the United States.

Preferred Qualifications:

5+ years of experience in Machine Learning Engineering, AI Engineering, Software Engineering, or a related field, with a proven track record of building and deploying production-grade AI and machine learning solutions.
Experience designing and developing Agentic AI applications, LLM-powered solutions, retrieval-augmented generation (RAG) systems, and intelligent automation workflows.
Strong experience with knowledge graphs, graph engineering, network analysis, semantic search, and building enterprise knowledge layers that connect structured and unstructured data to enable AI and analytics use cases.
Experience developing scalable data pipelines, data products, and feedback loop architectures that support continuous model and agent improvement.
Proficiency in Python and modern AI/ML frameworks and libraries such as PyTorch, TensorFlow, Scikit-learn, Pandas, and related technologies.
Experience with cloud-native AI/ML platforms and infrastructure, preferably Google Cloud Platform (Vertex AI, BigQuery, BigQuery ML), including model deployment, monitoring, and MLOps practices.
Experience building and supporting AI infrastructure, including vector databases, model serving platforms, APIs, microservices, distributed systems, and high-availability architectures.
Strong understanding of software engineering best practices, including CI/CD, version control, automated testing, security, and performance optimization.
Experience working with large-scale structured and unstructured datasets, SQL, NoSQL, and modern data architecture patterns.
Strong communication, collaboration, and stakeholder management skills with the ability to influence technical decisions across engineering, data, analytics, and product teams.
Demonstrated ability to thrive in ambiguous environments, rapidly learn emerging technologies, solve complex problems, and drive innovation in a fast-paced organization.