Machine Learning Engineer II

October 16

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The Home Depot

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retail • home improvement • customer service

10,000+

💰 Debt Financing on 2007-07

Description

• 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. • ML Engineers are expected to pair daily as they work through user stories and support products as they evolve. • May be involved in designing and implementing AI/ML algorithms to embed directly into software products. • This role will review submitted code and provide feedback to improve, based on best practices.

Requirements

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