[Hiring] Staff Machine Learning Engineer @Phantom

Role Description

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.

Key Responsibilities

• 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

• 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 methodologies.

Core Competencies

• 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.

Benefits

• Competitive salary and equity
• Comprehensive insurance (medical/dental/vision) 100% covered
• Stipend for your ideal remote / WFH set-up: laptop, headphones, and any other work gear you may need
• Flexible hours and a long-standing, supportive remote environment
• Unlimited vacation: Take time when you need it (and we really mean it!)
• 401(k) retirement plan
• Wellness benefit
• Daily lunch benefit