As a Cloud Machine Learning Engineer, you will play a critical role in ensuring customers have the best experience moving to the Google Cloud Machine Learning (ML) suite of products. You will design and implement machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and CloudML Engine.
You will work with customers to identify opportunities to apply ML in their business, and will travel to customer sites to deploy solutions and deliver workshops designed to educate and empower customers to realize the full potential of Google Cloud. You will have access to Google’s incredible technology to monitor application performance, debug and troubleshoot product code, and address customer and partner needs.
The Google Cloud Platform team helps customers transform and evolve their business through the use of Google’s global network, web-scale data centers and software infrastructure. As part of an entrepreneurial team in this rapidly growing business, you will help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.
- Act as a trusted technical advisor to customers and solve complex Machine Learning challenges.
- Create and deliver best practices recommendations, tutorials, blog articles, sample code, and technical presentations adapting to different levels of key business and technical stakeholders.
- Travel up to 30% of the time.
- Communicate effectively via video conferencing for meetings, technical reviews and onsite delivery activities.
- BA/BS degree in Computer Science, Mathematics or related technical field, or equivalent practical experience.
- Experience building machine learning solutions.
- Experience writing software in one or more languages such as Python, Scala, R and/or similar. Experience working with data structures, algorithms and software design.
- Experience working with technical customers.
- Experience working with recommendation engines, data pipelines and/or distributed machine learning.
- Experience with deep learning frameworks (such as l, Torch, Caffe, Theano, etc).
- Experience in technical consulting.
- Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ELT and reporting/analytic tools and environments (such as Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume).