Job Description
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
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.Whether it is paying online with Autofill, using tap and pay in stores, or using the Google Pay app, the Payments team at Google is focused on making payments simple, seamless, and secure. In addition to consumer payment technologies, the Payments team also powers the money movement between Google and its consumers and businesses.Responsibilities
- Analyze data for statistical insights.
- Develop accurate and fair machine learning models.
- Design experiments and implement new features and metrics.
- Work with product and marketing teams for experimentation and launch.
- Build and maintain production infrastructure, ensuring critical services and data are available and reliable.
