• Build a high-performing ML team: Lead hiring, onboarding, performance management, and career development. Set the frameworks, operating rhythms, and succession plans that give ML engineers clarity, support, and room to grow. You are the person who makes sure the team is getting the leadership attention it needs. • Own the team's operating systems: Build and maintain the rituals and structures that keep the team effective: sprint cadences, incident review, model monitoring feedback loops, cross-team reporting, and the prioritisation processes that keep the team focused on what matters. • Enable technical excellence without owning it: Act as a sounding board for ML engineers on questions and blockers. Ask the right questions, help surface risks, and create space for the experts to make good decisions, without adding another review layer or positioning yourself as a technical authority. • Drive collaboration with the Rosso Engineering Manager: Partner closely to align priorities between the ML and software engineering teams within the service. The two teams need to work together effectively, and you are a key part of making that happen. • Represent the team and champion tem's culture: Communicate progress, risks, and priorities clearly to the broader organisation. Lead with intention in a fully remote environment, champion what excellent remote work looks like, and represent tem in the ML community when recruiting and engaging externally.

• Build and maintain ETL/ELT pipelines that move data from source systems into Snowflake via Fivetran and dbt. • Clean, validate, and transform raw data into well-modeled datasets ready for analytics and ML workloads. • Monitor pipeline health and troubleshoot data quality issues as they arise. • Build, fine-tune, and evaluate AI/ML models and LLM-based solutions for specific business problems. • Design and run evaluation frameworks to measure model performance, detect hallucinations and bias. • Collaborate with Product and Engineering to ship AI-powered features into the GLOBO platform. • Implement guardrails, monitoring, and alerting for AI services in production. • Monitor inference latency, token usage, and compute costs across AI services and data infrastructure.

• Define the technical roadmap for visual personalization, bridging the gap between ML science and UI/UX design • Architect and deploy multi-armed bandit (MAB) systems to dynamically select the best creative assets for show tiles and hero marquees • Develop models to personalize carousel design types (e.g., square vs. poster), presentation styles, and even the ordering of attributes shown to the user • Utilize NLP and LLMs to experiment with and optimize carousel titles and content descriptions based on user preferences and trending topics • Design "always-on" experimentation pipelines that can shift traffic based on model confidence and reward signals (CTR, Playback Start) • Partner with Content Engineering to leverage visual embeddings and computer vision signals to understand why certain artwork performs better than others

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