AI/ML Engineer - Work

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At Cengage, our employees have a direct impact in helping students around the world discover the power and joy of learning. We are bonded by our shared purpose – driving innovation that helps millions of learners improve their lives and achieve their dreams through education.

Cengage's portfolio of businesses supports student choice by providing a range of pathways that help learners achieve their goals and lead a choice-filled life.

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Our business is driven by our strong culture, and we know that creating an inclusive workplace is absolutely essential to the success of our company and our learners, as well as our individual well-being. We recognize the value of diverse perspectives in everything we do, and strive to ensure employees of all levels and backgrounds feel empowered to voice their ideas and bring their authentic selves to work. We achieve these priorities through programs, benefits, and initiatives that are integrated into the fabric of how we work every day. To learn more, please see .

The AI/ML Engineer – Work builds AI-driven workforce and skills-based capabilities for Cengage's career and learning products. You will develop the models and systems that infer skills, verify competencies, and power skills-based matching and recommendations — the capabilities that underpin Cengage's skills graph and workforce platforms including Skills Verification.

This role requires a builder who is excited about applied ML for skills, career, and learning data. The ideal candidate has worked on matching, ranking, recommendation, or representation learning problems, understands the workforce and skills domain, and can ship production ML features that meaningfully improve learner career outcomes.

Key Responsibilities

Skills & Workforce AI Development

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Develop skills inference models that extract competencies from content, assessments, and learner activity

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Build skills verification models powering the Skills Verification platform

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Create skills-based matching and recommendation systems for jobs, courses, and learning paths

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Develop career pathway recommendation and skills gap analysis features

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Integrate AI into Cengage workforce platforms including Infosec Skills and IQ

Platform Integration & Engineering

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Integrate AI into workforce platforms including content, assessment, and lab systems

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Enable skills-based matching and recommendations across Cengage's workforce ecosystem

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Partner with platform engineering on API design, scaling, and production deployment

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Align to the NICE Framework and other recognized skills taxonomies where applicable

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Build evaluation and monitoring systems to measure and improve model accuracy and performance

Measurement & Business Impact

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Track skills verification accuracy, recommendation quality, and adoption of AI-driven career tools

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Partner with product and data science on offline and online evaluation

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Drive integration completeness across Cengage's workforce product suite

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Maintain feature delivery cadence with weekly shipping discipline

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Partner with Governance on patent and IP considerations for novel approaches

Required Qualifications

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Bachelor's degree in Computer Science, Engineering, or related field

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4+ years of experience in software engineering, with at least 2 years focused on AI/ML

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Strong proficiency in Python with experience building production ML systems

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Hands-on experience with modern ML techniques including embeddings, ranking, and LLMs

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Experience with recommendation systems, matching, or representation learning

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Solid software engineering fundamentals including testing, CI/CD, and system design

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Experience with offline and online model evaluation

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Strong communication skills to work with product, data science, and platform teams

Preferred Qualifications

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Prior experience in workforce tech, HR tech, or skills-based matching platforms

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Familiarity with skills taxonomies (NICE Framework, O*NET, ESCO, Lightcast)

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Experience with LLMs for classification, extraction, and zero-shot matching

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Background in cybersecurity education or adjacent technical learning domains

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Experience with behavioral pattern analysis or competency assessment

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Familiarity with agentic AI frameworks (LangChain, LlamaIndex)

Tools & Technologies

You should be comfortable with many of the following:

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Languages: Python, JavaScript/TypeScript, SQL

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AI/ML: PyTorch, TensorFlow, Hugging Face, OpenAI API, Anthropic API

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ML Infra: AWS SageMaker, MLflow, Weights & Biases, Ray

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Vector DBs: Pinecone, Weaviate, pgvector

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Data: Snowflake, Databricks, Postgres, Spark

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DevOps: Docker, Terraform, GitHub Actions, CI/CD pipelines

Key Competencies

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ML Craft — fluent across classical ML and modern LLM-based approaches

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Product Orientation — connects model improvements to learner and user outcomes

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Shipping Mindset — delivers on