Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, and/or cross-business projects.
  
• 2 years of experience building AI/ML platforms for developers, for deep neural networks and/or Gen AI. Experience with the entire ML lifecycle from experimentation to monitoring and A/B testing.
  
• 1 year of experience in a technical leadership role.
• Experience delivering cost and efficiency improvements.
• Proficient with any one of the following: Ray, Metaflow, LangChain. Familiarity with open-source development and community engagement.
  

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.

The GKE AI Ecosystem Tools and Frameworks team is dedicated to making Kubernetes the go-to platform for AI/ML workloads. We achieve this by identifying and driving key improvements to open-source software (OSS) in the Gen AI development ecosystem such as Ray, collaborating extensively with internal Google teams for core Kubernetes contributions and the OSS community, and providing reference architectures and solutions that showcase GCP platform value for AI/ML with GKE (e.g. for RAG ).

We are focusing on Ray, Metaflow, and LangChain initially, while developing a sustainable framework for future AI/ML framework integrations with Kubernetes. Through these efforts, we empower developers to seamlessly build, train, and deploy AI models on GKE.

Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $161,000-$239,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

• Write and test product or system development code.
  Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
  
  
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
• Explore emerging AI/ML frameworks and user journeys to identify opportunities for improving Kubernetes support, as well as generalize patterns across different frameworks
  
• Analyze shortcomings in frameworks, Kubernetes, and the overall developer lifecycle to enhance AI workflows and propose/drive solutions to address these challenges. Engage with internal Google teams and open source communities to foster collaboration and drive improvements.