Software Engineer, Google Kubernetes Engine AI Training
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Minimum qualifications:
- Bachelor's degree in Computer Science, related technical field, or equivalent practical experience.
- Candidates will typically have 5 years of experience with data structures/algorithms and software development in one or more programming languages.
- Typically 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- Typically 2 years of experience in a technical leadership role for projects.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or technical related field.
- Experience developing accessible technologies.
- Experience with OSS projects and community collaboration.
- Experience leading, designing, and implementing large-scale distributed systems.
- Experience with AI frameworks (e.g., Tensorflow, PyTorch, JAX), algorithms, and tools (e.g., Kubeflow, Ray.io, MLflow).
- Track record of ideation and innovating technology at scale.
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.Responsibilities
- Design and implement solutions that improve performance, scalability, and resource obtainability for the most complex GenAI training workloads.
- Ensure the delivery of high-quality code while adhering to best practices in software engineering.
- Participate in Agile processes, employing methodologies like Scrum.
- Collaborate with cross-functional teams (product management, other engineering groups, and OSS community) to make strategic decisions impacting Kubernetes evolution towards the best platform for AI/ML workloads.
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