Responsibilities:

  • 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 for personalization, notification dispatch, and onboarding flows.
  • Evaluate and integrate ML techniques including multi-armed bandits, reinforcement learning, LLMs for content generation, and graph neural networks.
  • Design, train, and validate models for churn propensity, lifetime value (LTV) forecasting, next-best-action, and lookalike modeling.
  • Build and optimize recommendation engines and semantic search systems.
  • Establish experimentation frameworks using advanced A/B testing, causal inference, and multivariate testing.
  • Partner with Product and Growth Marketing to translate business hypotheses into machine learning problems.
  • Mentor senior engineers across data and ML organizations.
  • Advocate for ML engineering best practices, including model monitoring, feature stores, reproducible training pipelines, and data governance.

Requirements:

  • 8+ years of experience in machine learning engineering, data science, or software engineering, including at least 3+ years in a Staff, Principal, or Tech Lead capacity.
  • Experience building and scaling ML systems in growth, marketing technology, recommendation engines, or consumer engagement.
  • Extensive experience with large-scale data processing and distributed computing.
  • Expert-level proficiency in Python, Scala, or Java.
  • Experience with PyTorch, TensorFlow, JAX, or XGBoost.
  • Experience with Spark, Flink, Kafka, Snowflake or BigQuery, Ray, Kubeflow, MLflow, or SageMaker.
  • Deep understanding of causal inference, uplift modeling, and statistical testing.
  • Ability to connect algorithmic improvements to business metrics such as MAU/DAU, conversion rates, and retention.
  • Strong communication skills for explaining technical architectures and algorithmic choices to non-technical stakeholders and executives.

Employment: Full-time. Remote.