Senior Machine Learning Research Engineer

Pearson Education - Hoboken, NJ

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)