Machine Learning Engineer

Robotics Technologies - San Jose, CA

Full-time - Mid Level

San Jose, CA

Merchant Wholesalers, Durable Goods

About the position

The Machine Learning Engineer position at Robotics Technologies LLC is a critical role focused on developing scalable data processing pipelines for analytical and predictive platform services. This position requires collaboration with other data scientists and engineers to address technical challenges effectively. The engineer will provide recommendations and guidance to support the development roadmap of Pearson's GLP product. Additionally, the role involves working closely with engineers to build, test, deploy, and troubleshoot machine learning and algorithm-based software. The ideal candidate will have a strong background in machine learning and data science, with a focus on practical engineering solutions that translate customer goals into actionable outcomes. The position is based in San Jose, California, and is expected to last for a duration of 8 months. As a W-2 employee, the consultant must be on the company payroll, and Corp-to-Corp arrangements are not permitted. This role is an excellent opportunity for individuals looking to apply their expertise in a dynamic environment while contributing to innovative projects in the field of machine learning.

Responsibilities

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