What You'll Do
•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
•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
•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
What We're Looking For
•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
•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
•Business Acumen: Ability to directly connect algorithmic improvements to top-line growth metrics (e.g., MAU/DAU, conversion rates, retention curves)
•Communication: Exceptional ability to explain highly complex technical architectures and algorithmic choices to non-technical stakeholders and executives
Context
•Wallets play a pivotal role: Wallets are responsible for on-boarding new users into crypto, and can make or break the user experience
•We are moving to a multi-chain world: New blockchains and scaling solutions are coming online and gaining traction, but are lacking decent wallets and bridges
•DeFi & NFTs are exploding: Interest in DeFi and NFTs has exploded, yet they are still an after-thought in existing wallets
