AI / Machine Learning Engineer II

• End-to-end ML ownership: Independently lead applied machine learning initiatives from data preparation and model development

through experimentation, production deployment, monitoring, and continuous optimization.

• Productionization and MLOps: Deploy and operate scalable ML solutions with robust workflows for batch or real-time inference,

evaluation, monitoring, observability, versioning, retraining, rollback, and continuous model iteration.

• Experimentation and impact measurement: Design and analyze A/B tests, holdouts, and validation frameworks to measure

incremental customer and business outcomes.

• Advanced model development: Design and build propensity, response, uplift, recommendation and ranking, contextual bandit,

segmentation, optimization, and customer-value models.

• Cross-functional delivery: Partner with ML infrastructure, data engineering, backend engineering, product, analytics, and business

teams to integrate models into reliable production systems.

• AI-first engineering workflows: Build agentic tools, automation, and reusable modules that streamline model development and MLOps

workflows, improve productivity, and increase the speed, quality, and consistency of ML delivery.

Education: 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. Experience:

• 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

customer data.

• 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 workflows.

• Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual

bandits, pricing, optimization, or lifecycle decisioning is a strong plus. Skills:

• 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. Personal Attributes:

• 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

business value.

• 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.

Our hiring process includes the following steps:

1. Video Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience.
2. Technical interview: Demonstrate your applied machine learning, analytical, and engineering capabilities.
3. Hiring manager interview: Meet with the hiring manager to discuss your background and fit for the role.
4. Final interview: Meet with our AI leadership, including the Chief AI Officer, for a final assessment.