• Understand client’s business use cases and technical requirements • Map decisions with requirements and translate to developers • Identify solutions and narrow down the best option • Define guidelines and benchmarks for NFR considerations • Write and review design documents • Review architecture and design for best practices • Develop and design overall solutions for requirements • Relate technology integration scenarios to projects • Resolve issues during code/review through root cause analysis • Carry out POCs to ensure design/technology meets requirements

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: - 2 - 4 years of relevant work experience - Experience to algorithms such as clustering, forecasting, anomaly detection, and neural networks. - Experience to basic statistics and regression algorithms - Experience with Data Analysis and Machine Learning Tools and Libraries like Jupyter Notebooks, Pandas, SciPy, Scikit-learn, Gensim, tensorflow, pytorch, etc - Experience in Google Cloud Platform and AI/ML related components such as Vertex AI, BigQueryML, and Experience in effective data engineering practices and big data platforms such as BigQuery, Data Store, etc- Experience in a modern scripting language (preferably Python) - Experience with GPU acceleration (i.e. CUDA and cuDNN) - 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) - Experience in a Linux or Unix based environment - Experience in a CI/CD toolchain - Experience in REST and effective web service design - Familiarity with production systems design including High Availability, Disaster Recovery, Performance, Efficiency, and Security - Familiarity with NoSQL databases - Familiarity with cloud computing platform and associated automation patterns and machine learning services they provide - Familiarity with defensive coding practices and patterns for high Availability - Familiarity with A/B testing and effective REST design for scalable web services architecture - Familiarity with advanced machine learning techniques such as NLP, convolutional neural networks, autoencoders, and embeddings generation and utilization 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 - $180,000

• Design and implement pipelines for training, evaluating, deploying, and monitoring retrieval models • Work closely with ML Scientists to productionise recommendation models, improving reliability, latency, and observability • Build and optimise embedding generation and recommendations serving • Partner with backend and product teams to define integration requirements and coordinate deployments of recommendation services • Help extend the recommendations ML infrastructure in collaboration with MLOps, including: • Reproducible training workflows • CI/CD for model deployment • Real-time and batch model serving • Online/offline feature consistency • Monitoring and alerting • Maintain high standards for operational excellence, testing, and incident response • Contribute to a strong engineering culture focused on scalability, experimentation, and measurable impact

• Design and implement pipelines for training, evaluating, deploying, and monitoring learning-to-rank models. • Work closely with ML Scientists to productionise ranking models, improving reliability, latency, and observability. • Build and optimise real-time model serving systems that deliver personalised rankings across the app. • Partner with backend and product teams to define integration requirements and coordinate deployment of ranking services. • Help extend the ML infrastructure for ranking systems in collaboration with the MLOps team, including: • Reproducible model training workflows • CI/CD pipelines for model deployment • Real-time and batch model serving • Online/offline feature consistency through the feature store • Monitoring and alerting for production models • Maintain high standards for operational excellence, including testing, monitoring, maintenance, and incident response. • Contribute to a strong engineering culture focused on scalability, experimentation, and measurable impact.