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ML Engineer - Automated Scorer
ML Engineer - Automated ScorerPearson • Lincoln, NE, US
ML Engineer - Automated Scorer

ML Engineer - Automated Scorer

Pearson • Lincoln, NE, US
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Overview

Location : Remote - US

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, and 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.

Note : This description summarizes the responsibilities and qualifications for the Machine Learning Engineer role based on the information provided.

Responsibilities

  • 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

Qualifications

  • Bachelor's degree in a quantitative field (CS, EE, statistics, math, data science)
  • 0-2 years professional experience as a software engineer or data scientist
  • Solid understanding of machine learning principles and current / emerging technologies
  • Strong coding & analytics skills including proficiency in Python and Linux commands
  • Understanding of or experience with deploying machine learning models into production environments
  • Familiarity with software engineering fundamentals (version control, object-oriented and functional programming, database and API access patterns, testing)
  • Passionate about agile software processes, data-driven development, reliability, and systematic experimentation
  • Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities
  • Curious and always learning habits of mind
  • Strong team-oriented approach to work, with excellent interpersonal and communication skills, both oral and written
  • Ability to work effectively as a member of a team in a collaborative environment
  • Demonstrated ability to manage multiple tasks and projects simultaneously

    • Experiences That Will Set You Apart

  • Advanced degree in a quantitative field (CS, EE, statistics, math, data science)
  • Track record of producing machine learning models and production infrastructure at scale
  • Familiarity with traditional natural language processing (NLP) techniques and / or latest advancements in large language models (LLMs), generative AI, active learning and reinforcement learning
  • Strong experience with machine learning in non-NLP domains
  • Experience using containerized technologies such as Docker and / or Kubernetes

    • Working location

    This position is remote

    Compensation and Contact

    Compensation at Pearson is influenced by a wide array of factors including but not limited to skill set, level of experience, and location. The pay range for this position is $100,000 - $110,000, depending on location and other factors. This position is eligible to participate in an annual incentive program. Applications will be accepted through October 8, 2025. Information on benefits offered is available here.

    EEO Statement

    Pearson is an Equal Opportunity Employer and a member of E-Verify. Employment decisions are based on qualifications, merit and business need. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, sexual orientation, gender identity, gender expression, age, national origin, protected veteran status, disability status or any other group protected by law. Reasonable accommodations may be requested by emailing TalentExperienceGlobalTeam@grp.pearson.com.

    Job

    Evaluation

    Job Family

    Learning & Content Delivery

    Organization

    Assessment & Qualifications

    Schedule

    FULL_TIME

    Workplace Type

    Remote

    J-18808-Ljbffr

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