Machine Learning Engineer II (Remote)

The Home Depot

2d • $90,000 - $170,000 • Remote

About The Position

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

Nice To Haves

• 1 - 3 years of relevant work experience
• Experience in a modern scripting language (preferably Python)
• Experience in effective data engineering practices and big data platforms such as BigQuery, Data Store, etc
• Experience in modern web application framework such as Node.js
• Experience in a front-end technology and framework such as HTML, CCS, JavaScript, ReactJS, D3
• Experience in writing SQL queries against a relational database
• Experience in version control systems (preferable Git)
• Familiarity with algorithms such as clustering, forecasting, anomaly detection, and neural networks.
• Familiarity with basic statistics and regression algorithms
• Familiarity with advanced statistics such as bayesian statistics
• Familiarity with Data Analysis and Machine Learning Tools and Libraries like Jupyter Notebooks, Pandas, SciPy, Scikit-learn, Gensim, tensorflow, pytorch, etc.
• Familiarity with Google Cloud Platform and AI/ML related components such as Vertex AI, BigQueryML, and AutoML
• Familiarity with a Linux or Unix based environment
• Familiarity with a CI/CD toolchain
• Familiarity with REST and effective web service design
• Familiarity with production systems design including High Availability, Disaster Recovery, Performance, Efficiency, and Security

Responsibilities

• 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; 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
• 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
• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: High school or GED