Software Engineer, Machine Learning, Payments

About

Google's software engineers develop next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products handle information at massive scale and extend beyond web search. We seek engineers who bring fresh ideas from information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design, and mobile. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as our fast-paced business grows and evolves. We value versatility, leadership, and enthusiasm for tackling new problems across the full stack as we push technology forward. The Payments team focuses on making payments simple, seamless, and secure, powering both consumer payment technologies and money movement between Google and its users and businesses.

Responsibilities

• Analyze data for statistical insights.
• Develop accurate and fair machine learning models.
• Design experiments and implement new features and metrics.
• Collaborate with product and marketing teams for experimentation and launch.
• Build and maintain production infrastructure, ensuring critical services and data are available and reliable.

Minimum qualifications

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages (e.g., Go, Python, C, C++, Java, JavaScript), or 1 year of experience with an advanced degree.
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, and debugging).

Preferred qualifications

• Master's degree or PhD in Computer Science or a related technical field.
• Experience in developing and maintaining machine learning models in production.
• Knowledge of machine learning techniques and applications.
• Experience in analyzing and improving efficiency, scalability, and stability of various systems.