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AI Engineer, Google Cloud


Full time



About the Role

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are engineered for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As a Cloud AI Engineer, you will design and implement machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and Vertex AI. You will work with customers to identify opportunities to apply machine learning in their business, travel to customer sites to deploy solutions, and deliver workshops to educate and empower customers. Additionally, you will work with Product Management and Product Engineering to build and constantly lead excellence in our products. You will also travel up to 30% of the time for meetings, technical reviews, and onsite delivery activities.

Google Cloud accelerates organizations’ ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology – all on the cleanest cloud in the industry. 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-$168,000 + 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.


  • Become a trusted technical advisor to customers and solve Machine Learning challenges.

  • Create and deliver best practices recommendations, tutorials, blog articles, sample code, and technical presentations adapting to different levels of business and technical stakeholders.

  • Work with customers, partners, and Google Product teams to deliver tailored solutions into production.

  • Mentor customers on the practical challenges in Machine Learning systems, including feature extraction, feature definition, data validation, monitoring, and management of features or models.

Minimum qualifications:

  • Bachelor's degree in Computer Science, Mathematics, or a related technical field, or equivalent practical experience.

  • Experience in building machine learning (ML) solutions.

  • Experience coding in one or more languages (e.g., Python, Scala, Java, Go, or similar) with experience in data structures, algorithms, and software design.

  • Experience working with technical customers.

Preferred qualifications:

  • Experience working with recommendation engines, data pipelines, or distributed machine learning.

  • Experience in technical consulting.

  • Experience with deep learning frameworks (e.g., l, Torch, Caffe, Theano).

  • Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, extract transform load (ETL), and reporting or analytic tools and environments (e.g., Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume).

  • Understanding of the auxiliary practical concerns in production ML systems.

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