About this role
The AI Engineer in Google Cloud’s Advanced Solutions Lab delivers and develops a machine learning and generative AI curriculum for global participants. The role works directly with customers on AI applications for business challenges and industry use cases, combining technical instruction with support for customer projects.
Responsibilities
- Deliver Advanced Solutions Lab content, lead participants’ daily learning, identify Google machine learning experts for specific sessions, and continuously improve the curriculum.
- Lead and support customer machine learning projects from problem framing through implementation. Design AI and machine learning learning materials based on market trends and customer needs, collaborating with cross-functional Google experts and recommending appropriate open-source frameworks and models.
- Stay current with machine learning developments and connect participants with expertise across the Google Cloud research community. Serve as a machine learning subject matter expert for Google Cloud Consulting through client-facing services, intellectual property development, public speaking, and bootcamps. Participate in research collaboration and engineering projects within the broader Google machine learning community.
Minimum qualifications: A bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience; 6 years of coding experience with one or more languages such as Java, C/C++, or Python; and 3 years of experience building production AI or machine learning models or agentic solutions for data including tabular data, images, video, speech, or unstructured text, using tools such as TensorFlow, Keras, JAX, Spark ML, or Scikit-learn. Experience conducting data and machine learning technical training in a client-facing consulting role and architecting solutions on Google Cloud Platform or another public cloud is also required.
Preferred qualifications
A master’s degree or PhD in Computer Science, Mathematics, or another quantitative field, or equivalent practical experience; machine learning and technology-area experience; experience learning new material and delivering it to clients and students; and knowledge of data warehouse architectures, infrastructure, ETL/ELT, and reporting or analytics tools such as Apache Beam, Hadoop, Spark, and Hive.
The listed locations are Chicago, Atlanta, and Austin. US compensation is $152,000–$221,000, plus a 15% bonus target, equity, and benefits. English proficiency is required.