Senior Machine Learning Research Engineer

$140,000 - $150,000/Yr

Pearson Education - Trenton, NJ

Full-time - Mid Level

Remote - Trenton, NJ

Publishing Industries

About the position

The Senior Machine Learning Research Engineer at Pearson is responsible for leading the design and development of machine learning capabilities, particularly in automated writing assessment. This role involves collaborating with cross-functional teams to create advanced machine learning systems that analyze learner exam responses, providing insights into student performance. The position is fully remote, allowing for flexibility while contributing to significant advancements in educational technology.

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 (e.g. software engineers, product managers, subject matter experts, learning scientists and interaction designers)
• Build and maintain software components (pipelines, 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 with 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 (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 any of the following: 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)

Benefits

• Annual incentive program
• Remote work flexibility
• Opportunities for growth and development