machine learning engineer for growth and engagement

Phantom connects people to open global markets, including perpetuals, prediction markets, tokenized assets, stablecoins, and memes. Its app provides access to real-time market data and verified trader performance, with self-custody and open networks at its core. Phantom also partners with companies in finance to make financial products accessible to everyone.

• 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 machine learning 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 forecasting, next-best-action, and lookalike modeling
• Build and optimize recommendation engines and semantic search systems to surface relevant content, products, or features
• Establish experimentation frameworks, including advanced A/B testing, causal inference, and multivariate testing, to validate model variants in production
• Partner with Product and Growth Marketing teams to translate business hypotheses into actionable machine learning problems
• Mentor and coach senior engineers across data and ML organizations
• Advocate for ML engineering best practices, including model monitoring, feature store utilization, reproducible training pipelines, and data governance

• 8+ Years of professional experience in machine learning engineering, data science, or software engineering
• At least 3+ years in a Staff, Principal, or Tech Lead capacity
• Proven experience building and scaling ML systems in growth, marketing tech, recommendation engines, or consumer engagement
• Extensive experience with large-scale data processing and distributed computing
• Expert-level Python, Scala, or Java
• Experience with PyTorch, TensorFlow, JAX, or XGBoost
• Experience with Spark, Flink, Kafka, Snowflake/BigQuery, Ray, Kubeflow, MLflow, or SageMaker
• Deep understanding of causal inference, uplift modeling, and statistical testing methodologies
• Ability to connect algorithmic improvements to business growth metrics, including MAU/DAU, conversion rates, and retention curves
• Ability to explain complex technical architectures and algorithmic choices to non-technical stakeholders and executives
• Будет плюсом: No additional preferred qualifications specified

• Competitive salary and equity
• Medical, dental, and vision insurance fully covered
• Stipend for a remote/WFH setup, including a laptop, headphones, and other work gear
• Flexible hours
• Unlimited vacation
• 401(K) retirement plan
• Wellness benefit
• Daily lunch benefit