Staff Machine Learning Engineer

Phantom is on a mission to connect the world to the freedom of open markets. Tens of millions of people all over the world use Phantom to access global markets that never close, including perpetuals, prediction markets, tokenized assets, stablecoins and memes. Phantom users are able to discover the markets that matter and the cultural moments that shape them, building conviction through real-time data and the verified performance of top traders. With self-custody and access to open networks at its core, Phantom lets them control their financial moves in the same app they use to safely store or spend money worldwide. Phantom has reached #1 in Google Play's finance category and consistently ranks in the top 50 apps across all categories. Phantom partners with many of the most trusted and influential names in finance like Hyperliquid, Stripe, Kalshi and Visa, to make the most popular and innovative financial products accessible to everyone. We are around 180 people, fully remote, backed by a $150M Series C investment from a16z, Sequoia Capital and Paradigm.

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.

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