W-2 Open Positions Need to be Filled Immediately. Consultant must be on our company payroll, Corp-to-Corp (C2C) is not allowed.
Candidates encouraged to apply directly using this portal. We do not accept resumes from other company/third-party recruiters.
Job Overview
Job ID: J36993
Specialized Area: Machine learning
Job Title: Machine Learning Engineer
Location: San Jose, CA
Duration: 8 Months
Employment Type: W-2 (Consultant must be on our company payroll. C2C is not allowed)
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 road map.
- Work closely with engineers to build, test, deploy, and troubleshoot machine learning/algorithm-based software.
QUALIFICATIONS:
- 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.
ARTIFICIAL INTELLIGENCE TECHNOLOGIES LLC is an equal opportunity employer inclusive of female, minority, disability and veterans, (M/F/D/V). Hiring, promotion, transfer, compensation, benefits, discipline, termination and all other employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity, age, disability, national origin, citizenship/immigration status, veteran status or any other protected status.
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