Senior Machine Learning Research Engineer

Overview

Join Pearson's Applied ML Research Team, dedicated to maintaining and extending leadership in automated writing analysis for various assessment product markets. This team collaborates with Operational Delivery and ML Platform teams to develop advanced machine learning systems that analyze and score millions of learner exam responses annually. This role offers the opportunity to make a significant impact on education by pushing the boundaries of current technology.

Role Summary

As a Senior Machine Learning Research Engineer, you will be instrumental in advancing our research agenda around automated writing assessment. This fully remote position allows you to work from anywhere in the United States, collaborating with a diverse team.

Key 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 both technical and non-technical audiences.
• Stay up-to-date with the latest advancements in NLP, machine learning, and educational technology.
• Publish research papers in relevant conferences and journals.

Qualifications

• Master's degree in a quantitative field (CS, EE, statistics, math) or equivalent work experience.
• Two or more years of practical experience in developing NLP and machine learning models.
• Proficiency with deep learning techniques and frameworks such as PyTorch or TensorFlow.
• Solid software engineering fundamentals including version control, object-oriented and functional programming, database and API access patterns, testing.
• Familiarity with cloud platforms and infrastructure (AWS, GCP, Azure) and distributed computing.

Bonus Qualifications

• Experience with 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.
• Publication history in relevant conferences and workshops (ACL, NeurIPS, ICML, AAAI, AI in Education, Intelligent Tutoring Systems, LAK).

What To Expect From Pearson

Pearson is committed to creating vibrant and enriching learning experiences designed for real-life impact. We value an inclusive culture where differences are embraced, opportunities are accessible, and individuals are supported in reaching their full potential. We are an Affirmative Action and Equal Opportunity Employer, striving for a workforce that reflects the diversity of our communities.