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 experience: Five or more years of professional experience in applied machine learning, data science, ML engineering,
applied statistics, or a related field, or equivalent demonstrated impact.
• Large-scale data: Experience building and evaluating models using large-scale behavioral, transactional, product, marketing, or
• Experimentation: Experience designing experiments, defining success metrics, measuring incrementality, interpreting results, and
translating findings into practical product or business decisions.
Gen | AI / Machine Learning Engineer II
• Production collaboration and ML operations: Experience partnering with engineering, product, analytics, and business teams to deploy
and operate production ML systems, including inference pipelines, monitoring, observability, retraining, and cloud-based MLOps
• Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual
bandits, pricing, optimization, or lifecycle decisioning is a strong plus.
• Machine learning and modeling: Strong Python skills and hands-on experience with common ML frameworks, supervised learning,
model selection, hyperparameter tuning, evaluation, and performance diagnosis.
• Data processing and feature engineering: Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data
collection, cleaning, preprocessing, exploration, and feature development.
• Analytics and experimentation: Strong statistical reasoning and practical knowledge of A/B testing, holdout design, causal
measurement, incrementality, statistical significance, and business-impact analysis.
• Production engineering and MLOps: Experience with cloud ML platforms, deployment pipelines, batch or real-time inference, CI/CD,
model registries, monitoring, observability, retraining, rollback, and scalable system design.
• Strong ownership: Takes responsibility for delivering high-quality solutions and measurable outcomes with limited oversight.
• Business-impact orientation: Connects modeling and engineering decisions to customer experience, product performance, and
• AI-first builder mindset: Enjoys coding, modeling, automating, and shipping while proactively using AI and agentic tools to improve
productivity and quality.
• Clear, collaborative communication: Communicates assumptions, tradeoffs, risks, and results effectively across ML, engineering,
product, analytics, and business teams.