Pearson Education - Springfield, IL

posted 3 months ago

Full-time - Mid Level
Remote - Springfield, IL
Publishing Industries

About the position

The Applied ML Research Team at Pearson is dedicated to maintaining and extending the company's leadership in automated writing analysis across various assessment product markets. This team collaborates closely with the Operational Delivery and ML Platform teams to develop advanced machine learning systems capable of analyzing and scoring tens of millions of learner exam responses each year. The technology we create provides quick and meaningful insights into student performance on standardized tests, thereby supporting educators, students, and parents in their educational journeys. By joining our team, you will have the opportunity to make a significant impact on education while pushing the boundaries of what current technology can achieve. As a Machine Learning Research Engineer, you will take on a pivotal role in advancing our research agenda focused on automated writing assessment. This position is fully remote, allowing you to work from anywhere within the United States while collaborating with a diverse team of professionals. Your responsibilities will include ideating, designing, researching, and developing natural language processing (NLP) and machine learning features, products, and services. You will work alongside cross-functional teams, including software engineers, product managers, subject matter experts, learning scientists, and interaction designers, to build and maintain software components such as pipelines and APIs. In this role, you will also design experiments and create datasets to monitor, evaluate, and improve both new and existing ML/AI models and services. Effective communication of model performance to both technical and non-technical audiences will be essential. Staying up-to-date with the latest advancements in natural language processing, machine learning, and educational technology is crucial, as is the expectation to publish research papers in relevant conferences and journals.

Responsibilities

  • Ideate, design, research and develop natural language processing and machine learning features, products and services
  • Collaborate with cross-functional teams including software engineers, product managers, subject matter experts, learning scientists, and interaction designers
  • Build and maintain software components such as pipelines and APIs
  • Design experiments and build datasets to monitor, evaluate and improve new and existing ML/AI models and services
  • Communicate model performance to technical and non-technical audiences
  • Stay up-to-date and share latest advancements in natural language processing, machine learning and educational technology
  • Publish research papers in machine learning and educational science conferences and journals.

Requirements

  • A master's degree in a quantitative field (CS, EE, statistics, math) or equivalent work experience
  • Two or more years of practical experience in developing natural language processing (NLP) and machine learning models
  • A proven track record in developing novel, AI/ML-backed solutions
  • Proficiency with deep learning techniques and common frameworks such as PyTorch or Tensorflow
  • Solid software engineering fundamentals including version control, object-oriented and functional programming, database and API access patterns, testing
  • A strong understanding of approaches to evaluating NLP and ML task performance
  • Familiarity with cloud platforms and infrastructure (AWS, GCP, Azure) and distributed computing
  • A dedication to ensuring equitable access to quality education and enhancing learning experiences for all students.

Nice-to-haves

  • Familiarity with latest advancements in large language models (LLMs), generative AI, active learning and/or reinforcement learning
  • Background in education, learning sciences, cognitive science, psychometrics
  • Experience with automated scoring of writing, generation of feedback and/or discourse analysis
  • Facility with containerized technologies such as Docker, Podman and/or Kubernetes
  • Ability to utilize data creatively and effectively to define new machine learning tasks
  • Publication history in relevant conferences and workshops (ACL, NeurIPS, ICML, AAAI, AI in Education, Intelligent Tutoring Systems, LAK)

Benefits

  • Eligible to participate in an annual incentive program
  • Comprehensive health insurance coverage
  • Flexible working hours
  • Remote work opportunities
  • Professional development and growth opportunities
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