Staff Machine Learning Engineer

Phantom is on a mission to connect the world to the freedom of open markets through self-custody and access to open networks. The Staff Machine Learning Engineer will lead the technical strategy, architecture, and execution of Growth and Engagement ML initiatives, building systems that improve user acquisition, retention, lifetime value, and product engagement. The role also involves owning complex ML model lifecycles, mentoring senior engineers, and collaborating with Product, Data Science, and Marketing leadership.

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 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

Requirements:

• 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