The Home Depot Logo

The Home Depot

SR. Machine Learning Engineer, Enterprise AI Systems

Posted One Month Ago
Remote
Hiring Remotely in Georgia, USA
100K-180K Annually
Senior level
Remote
Hiring Remotely in Georgia, USA
100K-180K Annually
Senior level
Senior ML Engineer joins a product team to design, build, deploy, and monitor production-grade AI/ML solutions. Responsibilities include algorithm and software design, pairing with teammates, creating scalable data pipelines and infrastructure, implementing model serving and monitoring, performing performance tuning and testing, and supporting product lifecycle and stakeholder collaboration.
The summary above was generated by AI

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.

Minimum Education:

  • The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED.

Preferred Education:

  • No additional education

Minimum Years of Work Experience:

  • 2

Preferred Years of Work Experience:

  • No additional years of experience

Minimum Leadership Experience:

  • None

Preferred Leadership Experience:

  • None

Certifications:

  • None

Competencies:

  • Global Perspective
  • Manages Ambiguity
  • Nimble Learning
  • Self-Development
  • Collaborates
  • Cultivates Innovation
  • Situational Adaptability
  • Communicates Effectively
  • Drives Results
  • Interpersonal Savvy

For California, Colorado, Connecticut, Rhode Island, Nevada, New York City, Ithaca (NY), Westchester County (NY), and Washington residents:
 

The pay range for this position is between $100,000.00 - $180,000.00

Similar Jobs

7 Minutes Ago
Easy Apply
Remote or Hybrid
United States
Easy Apply
232K-348K Annually
Senior level
232K-348K Annually
Senior level
Artificial Intelligence • Cloud • Software
Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.
Top Skills: AWSAzureBig Data FrameworksClickhouseConfluent PlatformData GovernanceData WarehousingETLGCPKafkaKafka StreamsSnowflakeTinybird
51 Minutes Ago
Remote
United States
130K-150K Annually
Senior level
130K-150K Annually
Senior level
Fintech • Financial Services
Lead the design and execution of global total rewards programs, including compensation bands, benefits strategy, equity and incentive plans, job leveling, pay equity, and annual compensation cycles. Advise executives, HR business partners, and talent acquisition using market data, financial modeling, and internal equity analysis. Optimize total rewards systems and programs across a growing global organization.
Top Skills: CandoriqPavePayscaleRippling
52 Minutes Ago
Remote
USA
90K-110K Annually
Mid level
90K-110K Annually
Mid level
Healthtech
Build and maintain Customer Success and Implementation playbooks, SOPs, training programs, knowledge bases, presentation materials, and scalable workflows. Partner cross-functionally to communicate process and product changes, improve automation and efficiency, identify customer risks, and measure enablement effectiveness. Support implementation, launch readiness, customer health, escalations, business reviews, renewals, and expansion.
Top Skills: NotionSalesforce

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account