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Form of workMinimum qualifications:
- Bachelor’s degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 4 years of experience coding in a general purpose coding language or in system design, and troubleshooting and advocating for customers' needs, and triaging technical issues.
- 4 years of experience with 2 or more of the following: Web Tech, Data/Big Data, Systems Admin, Machine Learning, Networking, Kubernetes, Oracle, SAP.
Preferred qualifications:
- 2 years of experience in recommendation systems, natural language processing, speech recognition, or computer vision.
- Experience developing and/or training models using machine learning technologies (e.g., Tensorflow, Keras, PyTorch).
- Experience with specific machine learning architectures (e.g., AlexNet, LSTM, Conformers, BERT, etc.).
- Experience with the production deployment of machine learning.
- Experience with exploratory data analysis, model development, and auxiliary practical concerns in production ML systems.
- Effective leadership and influencing skills in the application of AI or Machine Learning, with the ability to lead the design and implementation of AI-based solutions, web services, debugging tools.
About the job
As a Technical Solutions Engineer, you will be a part of a global team that provides support to help customers make the switch to Google Cloud. You will ensure we have the necessary tools, processes, and needed technical knowledge to resolve the issue.
In this role, you will troubleshoot technical problems for customers with a mix of debugging, networking, system administration, updating documentation, and when needed coding/scripting. You will make our products easier to adopt and use by making improvements to the product, tools, processes, and documentation. You'll help drive the success of Google Cloud by understanding and advocating for our customers’ issues.
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 $114,000-$167,000 + 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.
Responsibilities
- Be a thought leader in ML operations helping customers proactively. Work with immediate teams to increase operational efficiency and improve Product Supportability.
- Work with customers on their production ML deployments to resolve issues and achieve production readiness, availability, and scale. Partner with Product and engineering teams to improve products based on customer feedback.
- Manage customer problems through effective diagnosis, resolution, documentation, or implementation of investigation tools to increase productivity for customer issues on Google Cloud Platform products.
- Develop an in-depth understanding of Google Cloud’s AI/ML products/solutions and underlying architectures by troubleshooting, reproducing, and determining the root cause for customer reported issues, building tools for faster diagnosis.
- Act as consultant and subject matter expert for internal stakeholders in engineering, sales, and customer organizations to resolve technical deployment obstacles and improve Google Cloud.
