Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages, and with data structures/algorithms.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- Experience in Generative AI (e.g., Large Language Models, Multi-Modal, Large Vision Models).
- Experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning, natural language processing, or other ML discipline.
- Master's degree or PhD in Computer Science or related technical field.
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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.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
Responsibilities
- Drive and lead in the development and governance of the Conversational Search and Question and Answering stack, and address research and product teams' needs.
- Understand various use cases and pain points for conversational search, and work with the modeling team, collaborators in research, and production users to design and implement better quality models.
- Propose, design, and implement production-grade and scalable machine learning infrastructure that integrates well with the existing tech stack.