Contractors’ Warehouse —
Machine Learning Engineer II (Remote)
Location – Atlanta, GA
Job ID – Req119931
Category – Technology
Apply By – Applications are accepted on an ongoing basis
Location – Atlanta, GA
Job ID – Req119931
Category – Technology
Apply By – Applications are accepted on an ongoing basis
Company Overview
What’s the best place we’ve ever built? The place where we work. At Home Depot, our goal is to provide the highest level of service, the broadest selection of products, and the most competitive prices. As the world’s largest home improvement specialty retailer, we operate more than 2,200 retail stores across North America. And each of our associates are focused one thing — helping our customers build and improve their homes, businesses, and ultimately their lives.
Pay Range
The pay range for this position is between $100,000.00 – $150,000.00.
Starting wage may vary based on a number of factors including, but not limited to, the position being offered, location, education, training, and/or experience. The Home Depot offers additional competitive and non-financial benefits, which may include a performance-based bonus program or a profit sharing program depending on position.
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.
Key Responsibilities:
- 65% 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
- 15% 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:
- 1 – 3 years of relevant work experience
- Expertise in ML development and ML ops lifecycle
- Experience working with multiple leading ML models
- 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 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
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:
- 1
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
The application window is anticipated to be closed on October 15th, 2024
See more benefits: livetheorangelife.com
Paid Time Off Benefits
Salaried associates are eligible for 2 weeks of vacation in their first year; FT hourly will be eligible for 40 hours of paid vacation time after 6 months of continuous service; (for positions in Washington State, Spokane, and Tacoma only) – Salary and Temporary associates will earn 1 hour of sick time for every 40 hours worked; FT associates will earn 1 hour of sick time for every 40 hours worked or 4 hours per month, whichever is greater; (for positions in Seattle only) – Salary and Temporary associates will earn 1 hour of sick time for every 30 hours worked; FT associates will earn 1 hour of sick time for every 30 hours worked or 4 hours per month, whichever is greater.
Location – Atlanta, GA
Job ID – Req119931
Category – Technology
Apply By – Applications are accepted on an ongoing basis
Role Overview
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.
Key Responsibilities:
- 65% 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
- 15% 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:
- 1 – 3 years of relevant work experience
- Expertise in ML development and ML ops lifecycle
- Experience working with multiple leading ML models
- 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 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
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:
- 1
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
The application window is anticipated to be closed on October 15th, 2024
See more benefits: livetheorangelife.com
Paid Time Off Benefits
Salaried associates are eligible for 2 weeks of vacation in their first year; FT hourly will be eligible for 40 hours of paid vacation time after 6 months of continuous service; (for positions in Washington State, Spokane, and Tacoma only) – Salary and Temporary associates will earn 1 hour of sick time for every 40 hours worked; FT associates will earn 1 hour of sick time for every 40 hours worked or 4 hours per month, whichever is greater; (for positions in Seattle only) – Salary and Temporary associates will earn 1 hour of sick time for every 30 hours worked; FT associates will earn 1 hour of sick time for every 30 hours worked or 4 hours per month, whichever is greater.
Remote/virtual – An associate in a remote/virtual role typically is not required to work from a designated Home Depot location to complete their job duties. Limited or infrequent in-office presence may be required. We also refer to this as location – independent.
Learn more about our 4 different work locations. Additional information will be provided during the application process.
As part of the application process for this role, there will be an on-line assessment. The assessment usually takes about 17 minutes to complete. You will be directed to the assessment link immediately after submitting your application. Once you click on the link, you will need to complete it within 72 hours after starting it. You may stop and restart the assessment as many times as you like within the 72-hour time frame.
During the assessment, we’ll ask you questions about your approach to work and various work-related situations. The questions are based on characteristics that are related to performing successfully in hourly roles at The Home Depot, including:
- Professional Experience
- Learning Potential
- Responsibility
- Customer Focus
If you have a disability and would like to request an accommodation related to the assessment, or you would like to obtain more information about the assessment, click here to learn more.
Store Location
GA01
VIRTUAL
Atlanta, GA
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