[Hiring] AI/ML Engineer @Cengage Group

Role Description

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
  • Develop skills inference models that extract competencies from content, assessments, and learner activity
  • Build skills verification models powering the Skills Verification platform
  • Create skills-based matching and recommendation systems for jobs, courses, and learning paths
  • Develop career pathway recommendation and skills gap analysis features
  • Integrate AI into Cengage workforce platforms including Infosec Skills and IQ
• Platform Integration & Engineering
  • Integrate AI into workforce platforms including content, assessment, and lab systems
  • Enable skills-based matching and recommendations across Cengage's workforce ecosystem
  • Partner with platform engineering on API design, scaling, and production deployment
  • Align to the NICE Framework and other recognized skills taxonomies where applicable
  • Build evaluation and monitoring systems to measure and improve model accuracy and performance
• Measurement & Business Impact
  • Track skills verification accuracy, recommendation quality, and adoption of AI-driven career tools
  • Partner with product and data science on offline and online evaluation
  • Drive integration completeness across Cengage's workforce product suite
  • Maintain feature delivery cadence with weekly shipping discipline
  • Partner with Governance on patent and IP considerations for novel approaches

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field
• 4+ years of experience in software engineering, with at least 2 years focused on AI/ML
• Strong proficiency in Python with experience building production ML systems
• Hands-on experience with modern ML techniques including embeddings, ranking, and LLMs
• Experience with recommendation systems, matching, or representation learning
• Solid software engineering fundamentals including testing, CI/CD, and system design
• Experience with offline and online model evaluation
• Strong communication skills to work with product, data science, and platform teams

Preferred Qualifications

• Prior experience in workforce tech, HR tech, or skills-based matching platforms
• Familiarity with skills taxonomies (NICE Framework, O*NET, ESCO, Lightcast)
• Experience with LLMs for classification, extraction, and zero-shot matching
• Background in cybersecurity education or adjacent technical learning domains
• Experience with behavioral pattern analysis or competency assessment
• Familiarity with agentic AI frameworks (LangChain, LlamaIndex)

Tools & Technologies

• Languages: Python, JavaScript/TypeScript, SQL
• AI/ML: PyTorch, TensorFlow, Hugging Face, OpenAI API, Anthropic API
• ML Infra: AWS SageMaker, MLflow, Weights & Biases, Ray
• Vector DBs: Pinecone, Weaviate, pgvector
• Data: Snowflake, Databricks, Postgres, Spark
• DevOps: Docker, Terraform, GitHub Actions, CI/CD pipelines

Key Competencies

• ML Craft — fluent across classical ML and modern LLM-based approaches
• Product Orientation — connects model improvements to learner and user outcomes
• Shipping Mindset — delivers on weekly cadence with measured impact
• Domain Curiosity — invests in understanding skills, workforce, and career dynamics
• Evaluation Rigor — designs offline and online evaluation thoughtfully
• Collaboration — partners effectively with product, research, and platform engineering

Benefits

• Opportunity to shape AI at scale across a global learning company
• Direct impact on business outcomes, product, and workforce productivity
• Access to cutting-edge AI tools, platforms, and technologies
• Collaborative team environment focused on innovation and continuous improvement
• Competitive compensation and comprehensive benefits
• Professional development and learning opportunities
• Flexible work arrangements with remote/hybrid options