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Machine Learning Solutions Engineer, Google Cloud, Professional Services 342 views

Minimum Qualifications

    • Bachelor’s degree in Computer Science, related field or equivalent practical experience.
    • Experience building ML models for different use cases. Software engineering experience building production ML models with one/more of the following: TensorFlow, MXNET, Spark ML, Scikit Learn, etc.
    • Experience conducting Data or ML technical training or experience in a client-facing technical consulting role.

Preferred Qualifications

    • Master’s or Ph.D. degree in Computer Science, Mathematics or other quantitative fields.
    • Proficiency in one or more SQL database technologies such as PostgreSQL, MySQL, Oracle, MS SQL, etc.
    • Experience working in a technology area, while being comfortable working in a dynamic and sometimes ambiguous environment.
    • Experience with databases, schema design, and data processing technologies such as Hadoop, Spark, fluent, Storm, Splunk, New Relic.
    • A passion for learning and continued self-development. A track record of taking on new materials and successfully delivering them to clients/students.

About The Job

The Google Cloud team helps companies, schools, and government seamlessly make the switch to Google products and supports them along the way. You swiftly problem-solve technical issues for customers to show how their products can make businesses more productive, collaborative, and innovative. You work closely with a cross-functional team of web developers and systems administrators, not to mention a variety of both regional and international customers. Your relationships with customers are crucial in helping Google grow its Google Cloud business and in bringing product portfolio into companies around the world.

The Advanced Solutions Lab (ASL) for Machine Learning (ML) curve provides customers with the opportunity to work side by side with Google’s Machine Learning experts in an immersive experience that enables companies to address their highest impact business challenges.

As a Machine Learning Solutions Engineer, you will be focused on delivering and, at times creating, cutting-edge Machine Learning training for ASL participants. You will be working side by side with their customers as they explore how to apply Machine Learning to their specific business use cases. You will also manage the ‘day to day’ experience of the Advanced Solutions Lab.

You will work to improve the offering, such as recommendations on curriculum enhancements, identifying and engaging Google ML experts to support various sessions in the program, or making recommendations on pre-packaged ML models or projects for inclusion. You will also be a member of the wider Professional Service Organization (PSO) ML experts with the opportunity to work on client-facing projects to help customers benefit from Google Cloud Machine Learning solutions.

Google Cloud helps millions of employees and organizations empower their employees, serve their customers, and build what’s next for their business — all with technology built in the cloud. Their products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. And their teams are dedicated to helping customers and developers see the benefits of their technology come to life.

Responsibilities

    • Manage the success of each ML Advanced Solutions Lab experience by delivering training, identifying ML experts across Google to support specific sessions and providing ongoing curriculum feedback.
    • Stay abreast of developments in ML and network across the Google Cloud ML research community, to provide ASL participants with up to date knowledge and unique opportunities for highly interactive engagements with other Google ML experts.
    • Support the Google Cloud Professional Services Machine Learning practice in the delivery of client-facing services.
    • Act as an ML subject matter expert within the Google Cloud Professional Services team and support other ML activities as they arise (e.g., work to develop IP, be a speaker at external events, run ML boot camps).
    • Deliver new ML related PSO offerings which may go beyond the scope of the services available.

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