Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems

Design and implement production machine learning systems that transform customer behavior, product context, model outputs, and feedback loops into trusted signals for recommendations, ranking, risk-aware decisioning, growth, and customer intelligence.

Responsibilities

• Build and operate production ML systems that turn customer and product context into trusted signals, rankings, recommendations, and decision capabilities.
• Design production data and signal contracts defining intended use, freshness, provenance, confidence, eligibility, and calibration.
• Own ranking, retrieval, recommendation, search, propensity, and next-best-action systems end to end from feature generation through serving, experimentation, monitoring, and feedback loops.
• Evaluate customer and business impact beyond short-term conversion, including trust, fairness, access, risk, compliance, long-term engagement, and segment-level performance.
• Partner across product, growth, data, platform, modeling, risk, and compliance to translate ambiguous goals into measurable ML system designs.
• Use AI and agents to accelerate development, analysis, testing, documentation, and operations while exposing reusable capabilities to product services, internal tools, and AI-assisted workflows.

Requirements

• 12+ years building and operating production software and ML systems for business-critical products.
• Deep expertise in ranking and retrieval, recommendations, search, personalization, growth and lifecycle ML, customer intelligence, propensity, churn/LTV, next-best-action, or model-derived risk signals.
• Strong production ML judgment across feature pipelines, model serving, experimentation, monitoring, feedback loops, online/offline consistency, and reliable signal interfaces.
• Ability to evaluate impact beyond short-term conversion, including trust, fairness, access, risk, compliance, and long-term engagement.
• Experience using AI-assisted engineering tools with appropriate verification, testing, and review for customer-impacting systems.
• Experience with semantic retrieval, embeddings, two-tower models, graph features, LLM-powered retrieval or decision systems, entity resolution, or real-time personalization is nice to have.
• Experience with experimentation, online evaluation, interleaving, counterfactual evaluation, multi-objective optimization, or long-term holdouts is nice to have.
• Experience building reusable feature or signal platforms, decision services, customer intelligence layers, model-derived data products, or agent-assisted operations is nice to have.

Benefits

• Remote work
• Medical insurance
• Flexible time off
• Retirement savings plans
• Modern family planning