AI & Machine Learning Engineer I

Our team is a core part of Gen's AI transformation. We build machine learning solutions that improve customer growth, retention, personalization, pricing, recommendations, billing success, and long-term customer value.

We are looking for a hands-on AI / Machine Learning Engineer I to build models, analyze customer and product data, evaluate experiments, and help deploy practical ML solutions. You will own well-scoped projects and collaborate with experienced team members and cross-functional partners.

Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a plus.

- Applied ML ownership: Own well-defined machine learning projects from data exploration and model development through validation, deployment, and iteration.

- Model development: Build and improve predictive, recommendation, ranking, segmentation, uplift, and customer-value models for customer personalization and decisioning.

- Data and feature development: Prepare datasets, define modeling targets, develop features, and ensure data quality for training and evaluation.

- Experimentation and measurement: Design and analyze A/B tests, holdouts, and offline evaluations to measure model performance and business impact.

- Deployment and collaboration: Work with engineering, product, analytics, and business partners to integrate models into production and improve them based on results and feedback.

- AI-first development: Use AI coding assistants, automation, and reusable tools to improve the speed, quality, and consistency of modeling and analytical workflows.

- Degree requirements are flexible. A technical degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, Economics, Engineering, or a related field is helpful, but equivalent practical experience is equally valued. A Master's or PhD in a quantitative field is a plus, but not required.

- Applied ML and model development: Two or more years of professional experience in applied machine learning, data science, ML engineering, applied statistics, or a related field, including experience building and evaluating models with real-world data.

- Data analytics: Experience analyzing behavioral, transactional, product, marketing, or customer data and translating findings into practical insights or recommendations.

- Experimentation: Experience defining success metrics, analyzing experiments, evaluating model performance, and interpreting business impact.

- Collaborative delivery: Experience working with engineering, product, analytics, or business partners to deploy or apply data-driven solutions.

- Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, or lifecycle decisioning is a plus.

- Machine learning and modeling: Strong Python skills and practical knowledge of supervised learning, model selection, hyperparameter tuning, evaluation, and performance analysis.

- Data processing and feature engineering: Strong SQL skills and experience using platforms such as BigQuery, Spark, or similar tools for data extraction, cleaning, preprocessing, exploration, and feature development.

- Analytics and experimentation: Strong analytical and statistical reasoning, including A/B testing, holdout design, statistical significance, incrementally, and business-impact measurement.

- Technical tools and workflows: Familiarity with common ML libraries, cloud data or ML platforms, version control, and AI-assisted development tools.

- Ownership mindset: Takes responsibility for assigned work, follows through on commitments, and proactively addresses issues.

- Business-impact orientation: Connects modeling and analysis to customer experience and measurable outcomes.

- AI-first builder mindset: Enjoys modeling, analyzing, automating, and shipping while using AI tools to improve productivity and quality.

- Growth mindset: Learns quickly, seeks feedback, and continuously develops technical and business knowledge.

- Clear, collaborative communication: Communicates ideas, assumptions, results, and challenges effectively with technical and non-technical partners.

1. Video Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience.

2. Recruiter Interview: Meet with a Technical Recruiter to discuss your background and walk through the interview process.

3. Technical Interview: Demonstrate your applied machine learning, analytical, and technical capabilities.

4. Hiring Manager Interview: Meet with the hiring manager to discuss your background and fit for the role.

5. Final Interview: Meet with our AI leadership for a final assessment.