Pearson Education - Hoboken, NJ

posted about 1 month ago

Full-time - Senior
Remote - Hoboken, NJ
Publishing Industries

About the position

The Senior Machine Learning Research Engineer at Pearson is a pivotal role focused on advancing automated writing analysis technologies. This position involves end-to-end responsibility for the research and development of machine learning capabilities, particularly in natural language processing (NLP). The role is fully remote, allowing collaboration with a diverse team to create impactful educational solutions that enhance learning experiences for students and educators alike.

Responsibilities

  • Lead end-to-end design of machine learning and AI based capabilities
  • 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
  • Mentor others in best practices

Requirements

  • A master's degree in a quantitative field (CS, EE, statistics, math) or equivalent work experience
  • Four 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)
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