Machine Learning Research Engineer

$120,000 - $130,000/Yr

Pearson Education - Austin, TX

Full-time - Mid Level

Remote - Austin, TX

Publishing Industries

About the position

The Machine Learning Research Engineer will be part of the Applied ML Research Team at Pearson, focusing on advancing automated writing analysis for various assessment product markets. This role involves end-to-end responsibility for the research and development of machine learning capabilities, particularly in natural language processing. The position is fully remote, allowing collaboration with a diverse team to enhance educational technology and provide meaningful insights into student performance.

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 ML/AI models and services
• Communicate model performance to both technical and non-technical audiences
• Stay up-to-date and share 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 frameworks such as PyTorch or Tensorflow
• Solid software engineering fundamentals including version control, object-oriented and functional programming, database and API access patterns, and 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 advancements in large language models (LLMs), generative AI, active learning, and/or reinforcement learning
• Background in education, learning sciences, cognitive science, or 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 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

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