MACHINE LEARNING ENGINEER II (REMOTE)

The Home Depot•GEORGIA - VIRTUAL - GA01, GA

1d•$100,000 - $150,000•Remote

About The Position

The Machine Learning Engineer II 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. 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. ML Engineers should be able to operate independently with minimum guidance from others, although 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.

Requirements

Must be eighteen years of age or older.
Must be legally permitted to work in the United States.
1+ years of experience in Machine Learning, Data Science, Software Engineering, or a related field
The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED.

Nice To Haves

2+ years of experience in Machine Learning, Data Science, Software Engineering, or a related field
Experience developing, training, and deploying machine learning models in production environments
Hands-on experience with ML frameworks and tools such as Python, Scikit-learn, TensorFlow, PyTorch, Pandas, and Jupyter Notebooks
Experience with feature engineering, model evaluation, experimentation, and monitoring
Knowledge of machine learning techniques including classification, regression, clustering, forecasting, and anomaly detection
Experience working with cloud-based ML platforms, preferably Google Cloud Platform (GCP), Vertex AI, and BigQuery
Strong SQL and Python skills with experience working on large datasets
Familiarity with MLOps, CI/CD pipelines, Git, and Linux/Unix environments
Experience building and consuming REST APIs and integrating ML solutions into applications
Understanding of software engineering best practices, scalability, reliability, and system performance
Exposure to deep learning, NLP, Generative AI, or LLM-based solutions is a plus
Experience with A/B testing and data-driven decision making
No additional education
No additional years of experience
None

Responsibilities

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
Program configuration/modification and setup activities on large projects using HD approved methodology
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
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
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