Minimum qualifications:

• PhD in Computer Science, similar field, or equivalent practical experience.
  
• 15 years of software engineering experience building and working with systems in the technology organization.
  
• 10 years of experience in applying AI solutions and ML-related technologies to solve real-world AI/ML problems in consumer or enterprise applications.
  
• Experience with tuning Large Language Models and ML modeling.
  

Preferred qualifications:

• Experience with enterprise programs and cloud technologies.
  
• Experience in Large Model research and technologies.
  
• Technical expertise in deep learning.
• Proven track record of inter team/cross-functional leadership.
  
• Vigilant about technical trends, adapting quickly to new AI/ML technologies, and applying consumer or enterprise applications.
  

About the job

Google’s ML, Systems and Cloud AI (MSCA) organization builds the technical foundation behind Google’s products as well as for Google Cloud’s compute and AI/ML offerings. We manage the underlying design elements, developer platforms, product components, and infrastructure at Google. Core ML is the central machine learning platform team that provides ML software tools, services, solutions and infrastructure to all the Google product areas, including Search, Ads, Youtube, Cloud, Maps, etc.

Within the Cloud AI Organization, we focus on building highly differentiated, highly scalable, and easy-to-use machine learning products and services that enable our customers to transform their business with AI. Products such as Vertex, AutoML, TPU, and TensorFlow Enterprise comprise a portfolio that spans the needs of Data Scientists, Developers, and overall Infrastructure. We provide customers with state-of-the-art ML models and full ML model building/serving toolsets (i.e. MLOps) and management workflows to enable customers to turn ideas into high-quality production AI solutions across verticals.

As Distinguished Engineer, you are responsible for driving, deciding, and landing the technical roadmap for our Cloud AI large model platform. In this role, you are expected to add expertise AI technologies/infrastructure and guide our teams towards execution. You will partner with vertex platform engineering and product teams, and work with engineering managers to guide technical directions. You'll partner closely with various partner teams both within the product (e.g., Cloud DA, Cloud GKE) and cross-product partner teams (e.g., CoreML, RMI) on collaborations. In addition, you will work with customers, solutions architects, sales leads, and GTM colleagues to understand customers' use cases, and translate them into technical designs and implementations.

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 $323,000-$465,000 + 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 for new hire 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.

Google’s ML, Systems and Cloud AI (MSCA) organization builds the technical foundation behind Google’s products as well as for Google Cloud’s compute and AI/ML offerings. We manage the underlying design elements, developer platforms, product components, and infrastructure at Google. Core ML is the central machine learning platform team that provides ML software tools, services, solutions and infrastructure to all the Google product areas, including Search, Ads, Youtube, Cloud, Maps, etc.

Within the Cloud AI Organization, we focus on building highly differentiated, highly scalable, and easy-to-use machine learning products and services that enable our customers to transform their business with AI. Products such as Vertex, AutoML, TPU, and TensorFlow Enterprise comprise a portfolio that spans the needs of Data Scientists, Developers, and overall Infrastructure. We provide customers with state-of-the-art ML models and full ML model building/serving toolsets (i.e. MLOps) and management workflows to enable customers to turn ideas into high-quality production AI solutions across verticals.

As Distinguished Engineer, you are responsible for driving, deciding, and landing the technical roadmap for our Cloud AI large model platform. In this role, you are expected to add expertise AI technologies/infrastructure and guide our teams towards execution. You will partner with vertex platform engineering and product teams, and work with engineering managers to guide technical directions. You'll partner closely with various partner teams both within the product (e.g., Cloud DA, Cloud GKE) and cross-product partner teams (e.g., CoreML, RMI) on collaborations. In addition, you will work with customers, solutions architects, sales leads, and GTM colleagues to understand customers' use cases, and translate them into technical designs and implementations.

Responsibilities

• Define the technical vision for productionizing large models to Vertex and providing developer tuning tools to support GCP customers tailoring large models to their business use cases.  
  
• Partner with the Google Deepmind team to drive large model innovations, and bring SOTA large model technologies to the platform. 
  
• Consult on OKRs across platform teams, review designs, participate in (and help to resolve) technical discussions, and drive high quality extensible engineering investments for large models.
  
• Develop a structure of other technical leads in the area, both by defining technical goals and orienting teams around technical decisions they can make, and by providing development support to engineers in the area. 
  
• Provide leadership in a firefighting, Code Yellow, or high priority situation. Acts as a technical lead or execution supervisor for high-priority, tricky, or unaligned/super cross-functional projects.