ML Engineer - Automated Scorer
PearsonAbout the role
Location: Remote - US
About Pearson’s Automated Scoring Team
As the world's learning company, Pearson helps people make more of their lives through learning. We use our knowledge, passion, and reach to tackle the big problems in education and inspire a love of learning that lasts a lifetime. That is why we need smart people like you. Together, we can transform education and provide boundless opportunities for billions of learners worldwide.
The Automated Scoring team develops machine learning-based models that analyze tens of millions of learner exam responses each year. Our technology is unique and meaningful, providing results quickly on student performance on standardized tests. The Machine Learning Engineer will join Pearson’s Automated Scoring Team to provide support for the administration of Pearson’s automated scoring programs and support the execution of initiatives to innovate and improve the delivery of Pearson's automated scoring technologies. This role will report to and work closely with the Director of Automated Scoring, but it will also support program managers, quality assurance automation engineers, psychometricians, and various internal stakeholders to ensure the quality and reliability of our automated scoring systems.
Machine Learning Engineer’s Duties & Responsibilities
Listed below are the typical duties and responsibilities expected of an individual for the job title. The items specified below are a guideline of the minimum expectations for the job title; changes will be made on a case-by-case basis for individuals who show potential to take on more opportunities.
Train, evaluate, and deploy machine learning models tasked with scoring short answer and essay student responses to formative and summative test administrations from school districts nationwide
Monitor performance of deployed machine learning models to ensure consistent, fair, and unbiased scoring in real time and recalibrate deployed models as needed
Maintain, update, and improve code base used to train and deploy machine learning models
Evaluate historical model performance and conduct experiments exploring strategies to potentially improve team modeling techniques and approaches
Research and stay up-to-date on emerging technologies in the NLP space
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