ML Engineer - Automated Scorer

Pearson; Springfield (IL); Remote; USD 100,000 - 110,000; Full time

Job summary

A global education company is seeking a Machine Learning Engineer to join its Automated Scoring Team. This remote role involves training and deploying machine learning models, monitoring performance, and collaborating with various internal stakeholders to enhance automated scoring systems. The ideal candidate should have a Bachelor's degree in a quantitative field and strong skills in Python and machine learning principles. The position offers a competitive salary range of $100,000 - $110,000.

Qualifications

• 0-2 years professional experience as a software engineer or data scientist.
• Understanding of deploying machine learning models into production environments.
• Ability to manage multiple tasks and projects simultaneously.

Responsibilities

• Train and deploy machine learning models for scoring tests.
• Monitor and maintain performance of machine learning models.
• Evaluate historical model performance to improve techniques.

Skills

Machine learning principles

Proficiency in Python

Strong coding skills

Analytical skills

Communication skills

Team collaboration

Education

Bachelor’s degree in a quantitative field

Tools

Docker

Kubernetes

Job description

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

Qualifications

Qualified individuals will be required to work with dynamic teams driven by project delivery goals. They should possess the drive to learn and continuously improve on work performance. They must also be detail-oriented and eager to work with peers in producing quality output. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• 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

This position is remote

Compensation at Pearson is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific location. The minimum full-time salary range is between $100,000 - $110,000

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. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.