Staff Machine Learning Engineer

ROLE OVERVIEW We are seeking a visionary and hands-on Staff Machine Learning Engineer to lead the technical strategy, architecture, and execution of our Growth and Engagement ML initiatives. In this role, you will bridge the gap between advanced machine learning and business strategy, designing systems that drive user acquisition, retention, lifetime value (LTV), and deep product engagement. As a technical pillar of the engineering organization, you will own the end-to-end lifecycle of complex ML models, mentor senior engineers, and collaborate closely with Product, Data Science, and Marketing leadership to move core business metrics. Key Responsibilities Technical Leadership & Strategy - Define the long-term technical roadmap for Growth and Engagement ML systems, ensuring scalability, reliability, and measurable business impact. - Architect and deploy production-grade ML pipelines and real-time decisioning systems that power personalization, notification dispatch, and onboarding flows. - Evaluate and integrate cutting-edge ML techniques, including multi-armed bandits, reinforcement learning, LLMs for content generation, and advanced graph neural networks. Execution & Modeling - Design, train, and validate sophisticated models targeting user lifecycle stages: propensity to churn, lifetime value (LTV) forecasting, next-best-action, and lookalike modeling. - Build and optimize recommendation engines and semantic search systems to surface highly relevant content, products, or features to users. - Establish robust experimentation frameworks (advanced A/B testing, causal inference, and multi-variate testing) to rigorously validate model variants in production. Collaboration & Mentorship - Partner with Product and Growth marketing teams to translate high-level business hypotheses into precise, actionable machine learning problems. - Mentor and coach senior engineers across the data and ML organizations, fostering a culture of technical excellence and continuous learning. - Advocate for ML engineering best practices, including model monitoring, feature store utilization, reproducible training pipelines, and data governance. Qualifications & Skills Experience - 8+ years of professional experience in machine learning engineering, data science, or software engineering, with at least 3+ years in a Staff, Principal, or Tech Lead capacity. - Proven track record of building and scaling ML systems specifically within growth, marketing tech, recommendation engines, or consumer engagement domains. - Extensive experience with large-scale data processing and distributed computing. Technical Proficiencies - Languages: Expert-level Python, Scala, or Java. - ML Frameworks: PyTorch, TensorFlow, JAX, or XGBoost. - Data & MLOps Infrastructure: Spark, Flink, Kafka, Snowflake/BigQuery, Ray, Kubeflow, MLflow, or SageMaker. - Experimentation: Deep understanding of causal inference, uplift modeling, and robust statistical testing methodologies.

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