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Job Type
Job Details
- Bachelor’s degree or equivalent practical experience.
- 1 year of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
- 1 year of experience with data structures or algorithms.
- 1 year of experience implementing core ML concepts.
Preferred qualifications:
- Experience in developing infrastructure for AI/ML systems.
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.
The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.
- Work with external customers to understand their requirements, onboard them, help with load tests, bugs, and questions.
- Enable GenAI capabilities on Kubernetes so they could run on Google Distributed Cloud.
- Build highly available, low latency systems.
- Implement robust testing and validation processes to ensure consistent, reliable production services.
- Identify and address risks and dependencies.