Senior Software Engineer – Backend, Machine Learning

🔥 26 minutes ago

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The Home Depot

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1978

🏗️ Construction

📦 Logistics

🛒 Retail

💰 Debt Financing on 2007-07

Construction • Logistics • Retail

The Home Depot is a leading home improvement retailer, offering a wide range of building materials, home improvement products, lawn and garden products, and related services. The company operates both physical stores and an online platform, providing comprehensive solutions for DIY enthusiasts, professional contractors, and homeowners. The Home Depot is committed to diversity, equity, and inclusion, providing employment opportunities and benefits to a diverse workforce. Additionally, the company places a high emphasis on customer service and associate engagement to maintain its position as a trusted leader in the home improvement industry.

📋 Description

• Develops, tests, deploys, and maintains software • Takes on new opportunities and tough challenges with a sense of urgency, high energy and enthusiasm • Develops test suites (functional, destructive, etc) to enable success, rapid deployment of code to production • Collaborates with other team members in agile processes • Creates new and better ways for the organization to be successful • Works with the Product Team to ensure user stories are valuable, developer ready, easy to understand and testable • Delivers multi-mode communications that convey a clear understanding of the unique needs of different audiences • Helps grow junior engineers by providing guidance on modern software development frameworks

🎯 Requirements

• 3-5 years of experience • Strong in at least one of: Java, Scala, Python • Microservices — REST APIs, gRPC, event-driven architecture (Pub/Sub, Cloud Tasks) • System Design — Distributed systems, scalability, fault tolerance, API gateway patterns • GCP Core — Compute Engine, GKE, Cloud Run, Cloud Functions, IAM • BigQuery — Advanced SQL, partitioning, clustering, cost optimization • Pipelines — Dataflow (Apache Beam), Dataproc (Spark), batch and streaming • Storage — Bigtable, Cloud Storage, Firestore • Orchestration — Cloud Composer (Airflow), Workflows • Vertex AI — Model training, hyperparameter tuning, pipelines, model registry • Frameworks — TensorFlow, PyTorch, scikit-learn • MLOps — Model serving (Vertex AI Endpoints), monitoring, retraining pipelines • Feature Store — Vertex AI Feature Store • Search ML — Ranking, relevance tuning, embeddings (nice to have) • AI & LLM Gemini — Gemini API, Gemini Pro, multimodal usage • Vertex AI Generative AI — Model Garden, grounding, tuning • RAG — Retrieval Augmented Generation, Vector Search (Vertex AI Vector Search) • Agentic AI — Multi-agent frameworks, tool use, autonomous workflows

🏖️ Benefits

• Health insurance • 401(k) matching • Paid time off • Flexible work hours • Professional development opportunities