Staff Machine Learning Engineer – Generative AI (Remote)

The Home Depot

4d•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 Staff Machine Learning Engineer is responsible for joining cross-functional teams (product, design, Engineering, infrastructure) to build innovative GenAI application experiences and overall product lifecycle for a product that our users love. The engineering process is highly collaborative. Staff ML Engineers are expected to pair daily as they work through user stories and support products as they evolve. Generative AI Engineers may design and implement applications using large language models (LLMs) and other generative models to embed intelligent capabilities directly into software products. Activities may include prompt engineering, model integration, building Retrieval-Augmented Generation (RAG) pipelines, and developing scalable AI services. The role may interact with business stakeholders, infrastructure teams, and development teams to ensure business requirements are effectively addressed through generative AI solutions. The role may also support evaluation, performance optimization, testing, and monitoring of AI systems in production. Additional responsibilities may include working with domain data, improving prompts and AI workflows, and creating documentation or enablement materials for generative AI solutions.

Requirements

Must be eighteen years of age or older.
Must be legally permitted to work in the United States.

Nice To Haves

5 - 7 years of relevant work experience
Experience in Python and modern AI development frameworks
Experience building Generative AI applications using large language models (LLMs)
Experience with prompt engineering, prompt optimization, and prompt evaluation techniques
Experience integrating AI models through APIs from platforms such as Google, OpenAI or Anthropic
Experience with GenAI frameworks such as Google Agent Development Kit (ADK)
Experience implementing Retrieval-Augmented Generation (RAG) pipelines using vector databases
Experience working with vector databases such as google Vertex AI Search
Experience with building conversational AI systems, or AI assistants
Experience with responsible AI practices including bias mitigation and safety guardrails
Experience working with graph databases, knowledge ingestion pipelines, and data mesh architectures to enable scalable, connected, and queryable AI knowledge systems.
Experience implementing CI/CD pipelines, monitoring, and automated workflows for reliable AI model deployment and lifecycle management.
Experience with monitoring, evaluation, and optimization of production AI systems
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 a front-end technology and framework such as Node.js, HTML, CCS, JavaScript, ReactJS, D3
Experience in writing SQL queries against a relational database
Experience in advanced machine learning techniques such as NLP, convolutional neural networks, autoencoders, and embeddings generation and utilization
Experience in training machine learning models with extremely large datasets
Experience with Data Analysis and Machine Learning Tools and Libraries like Jupyter Notebooks, Pandas, SciPy, Scikit-learn, Gensim, tensorflow, pytorch, etc.
Familiarity with production systems design including High Availability, Disaster Recovery, Performance, Efficiency, and Security
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
Familiarity with advanced machine learning architectures GANs, GRU, LSTMs, RNNs, CNNs, style transfer

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, Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable; 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; Attends conferences and learns how to apply new innovations and technologies where appropriate
Strategy and Planning - Researches and analyzes business trends and behavioral data to identify opportunities for improvement and new initiatives; Leads the evaluation development and recommendation of specific technology products and platforms to provide cost-effective solutions that meet business and technology requirements; Researches and designs best fit infrastructure, network, database, security, and machine learning architectures for products; Proactively creates and maintains tools for monitoring and support; Participates in project planning and management across multiple efforts; Develops formal training courses
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