Staff Machine Learning Engineer

Join The Home Depot as a Staff Machine Learning Engineer in Atlanta with remote options. You will lead ML software engineers, provide technical leadership on scalable ML systems, and drive production deployment, monitoring, and lifecycle management of ML solutions at scale, in collaboration with product teams. This role offers a salary range of USD 120,000 to 190,000 per year, a strong benefits package, and the opportunity to shape ML initiatives across the organization.

Responsibilities

• Delivery and Execution - Collaborate with UX, engineering, and product management to design secure, reliable, and scalable machine learning solutions; ensure user stories are developer-ready, clear, and testable; tailor off-the-shelf solutions to evolving business needs; build dashboards, logging, alerting, and response workflows to capture and address issues proactively.
• Learning - Engage in ongoing learning of modern software design and ML practices; review articles, tutorials, and videos; attend conferences to apply new innovations where appropriate.
• Strategy and Planning - Analyze business trends and behavioral data to identify opportunities; lead evaluation and recommendation of technology products and platforms to meet requirements; design suitable infrastructure, data, security, and ML architectures; create and maintain tooling to support teams.
• Support and Enablement - Provide cross-team support, monitor tools, and promote collaboration; deliver production application support; monitor production service level objectives; assess performance, capacity, and prediction quality across code, infrastructure, data, and model outputs.

Requirements

• Minimum age: 18 years old.
• Legal authorization to work in the United States.
• Education: High School Diploma or GED.
• Experience: 3 years of relevant work experience.

Preferred Qualifications

• 3 to 6 years of relevant work experience with strong experience designing, training, evaluating, and deploying ML models in production, including batch and real-time inference.
• Experience with ML lifecycle management, including feature engineering, model versioning, experimentation, validation, and monitoring for data drift and performance degradation.
• Experience building and operating ML pipelines using cloud-native services, data platforms, and CI/CD for reproducible deployments.
• Solid understanding of applied statistics and model evaluation metrics, balancing accuracy, interpretability, latency, and cost.
• Experience with algorithms such as clustering, forecasting, anomaly detection, and neural networks; familiarity with NLP, CNNs, autoencoders, and embeddings.
• Experience training models on very large datasets; proficient with data analysis tools such as Jupyter Notebooks, Pandas, SciPy, Scikit-learn, Gensim, TensorFlow, PyTorch.
• Experience with Google Cloud Platform and AI/ML components such as Vertex AI, BigQueryML, AutoML; strong data engineering practices with BigQuery, Data Store.
• Proficiency in Python, SQL, Git, Linux/Unix; familiarity with CI/CD toolchains; REST and scalable web service design; production systems with high availability, disaster recovery, performance, security.
• Understanding of advanced ML architectures including GANs, GRU, LSTMs, RNNs, CNNs, and style transfer.

Technologies

• Google Cloud Platform
• Vertex AI
• BigQueryML, AutoML
• Jupyter Notebooks, Pandas, SciPy, Scikit-learn
• Gensim, TensorFlow, PyTorch
• Python, SQL, Git
• Linux, Unix
• BigQuery, Data Store
• REST, CI/CD
• GANs, GRU, LSTMs, RNNs, CNNs, Style transfer

Benefits

• Paid parental leave to bond with your new addition
• 401(K) savings plan with company match
• Merit increases and performance bonuses
• On-the-spot recognition and rewards for a job well done
• Bonus Eligible
• 401(k) Company Matching
• Employee Stock Purchase Program

Reporting structure

This position typically reports to a Software Engineer Manager or Senior Software Engineer Manager and has zero direct reports.

Travel

Typically requires overnight travel 5% to 20% of the time.

Physical and Working Conditions

Most of the time is spent seated in a comfortable indoor environment; there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.

Located in a comfortable indoor area with infrequent unpleasant conditions.