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
Program configuration/modification and setup activities on large projects using HD approved methodology
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
Participates in learning activities around modern software design, machine learning, and development core practices
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

1
3 years of relevant work experience
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 advanced statistics such as bayesian statistics
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.

Machine Learning Engineer II

Key skills