Machine learning Engineer

Cloud Analytics Technologies - San Jose, CA

Full-time

San Jose, CA

About the position

The Machine Learning Engineer will play a crucial role in developing scalable data processing pipelines for analytical and predictive platform services. This position is based in San Jose, CA, and is expected to last for a duration of 8 months. The engineer will collaborate closely with other data scientists and engineers to find effective solutions to various technical challenges that arise during the development process. The role involves providing recommendations, guidance, and options to support Pearson's GLP product development roadmap, ensuring that the engineering solutions align with customer goals and technical requirements. In this position, the engineer will work hand-in-hand with software engineers to build, test, deploy, and troubleshoot machine learning and algorithm-based software. This collaborative environment will require the engineer to communicate complex technical ideas effectively to non-technical audiences, ensuring that all stakeholders understand the implications of the engineering solutions being proposed. The role demands a strong understanding of foundational statistics concepts and algorithms, as well as the ability to access, manage, transfer, integrate, and analyze complex datasets, particularly using SQL or map-reduce techniques. The ideal candidate will have a strong programming background, with fluency in at least one of the following languages: Python, R, Java, Scala, or C/C++. Familiarity with machine learning libraries such as Spark ML, TensorFlow, scikit-learn, MLib, DLib, Pandas, or others like H2O and Databricks will be essential for success in this role. Overall, this position offers an exciting opportunity to contribute to innovative projects in the field of machine learning and data science.

Responsibilities

• Develop scalable data processing pipelines for analytical and predictive platform services.
• Collaborate with other data scientists and engineers to find effective solutions to technical challenges.
• Provide recommendations, guidance, and options to support Pearson's GLP product development roadmap.
• Work closely with engineers to build, test, deploy, and troubleshoot machine learning/algorithm-based software.

Requirements

• MSc or higher in computer science, statistics, mathematics, physical science, engineering, or a comparable related technical field.
• 5+ years of industry experience in engineering, data science, or related areas.
• Demonstrated mastery in communication of technical ideas to non-technical audiences.
• Ability to translate customer goals into practical engineering solutions.
• Good understanding of foundational statistics concepts and algorithms: linear/logistic regression, random forest, boosting, NNs, etc.
• Strong programming skills with fluency in at least one of Python or R, Java, Scala, C/C++.
• Ability to access, manage, transfer, integrate, and analyze complex datasets, especially using SQL or map-reduce techniques.

Nice-to-haves

• Familiarity with libraries such as Spark ML, TensorFlow, scikit-learn, MLib, DLib, Pandas, or others like H2O, Databricks.