Staff Machine Learning Engineer (Pricing)

Join GoFundMe as a Staff Machine Learning Engineer (Pricing). In this role, you will design, develop, and deploy machine learning systems that power pricing and monetization programs. You will own end-to-end ML systems for pricing optimization, collaborate with cross-functional teams, and mentor other engineers and scientists. The position requires strong proficiency in Python and ML libraries, as well as experience in building and shipping production machine learning systems.

• Concevoir, développer et déployer des systèmes d'apprentissage automatique pour optimiser les prix et les programmes de monétisation.
• Posséder l'ensemble des systèmes d'apprentissage automatique pour l'optimisation des prix, de la définition du problème au développement du modèle.
• Collaborer avec les équipes pour développer l'instrumentation et les pipelines d'événements nécessaires à la formation et à l'évaluation.
• This role requires strong end-to-end execution and deep expertise in building production ML systems (data → training → online inference → measurement) with rigorous experimentation and monitoring
• Strong leadership and mentoring skills and a proven ability to raise the bar on architecture, engineering quality, and operational rigor for ML-powered pricing systems
• Ability to break down ambiguous, high-impact problems, define crisp interfaces and success metrics, and deliver iteratively with strong stakeholder communication
• Strong proficiency in Python and ML libraries/frameworks such as PyTorch, TensorFlow, Scikit-learn, plus strong software engineering fundamentals (testing, code review, CI/CD, API design, performance, and reliability)
• Advanced degree (Master’s or Ph.D.) in Computer Science, Statistics, Data Science, or a related technical field is preferred
• 7+ years of hands-on experience building and shipping production machine learning systems, with demonstrated ownership of backend services and ML pipelines in a high-availability environment
• Experience designing and deploying real-time model serving (sub-100ms to low-hundreds ms latency targets), including containerization, scalable inference, feature retrieval, and safe rollout strategies (canaries, shadowing, backward-compatible schema evolution)
• Experience implementing ML monitoring for both technical and business metrics (drift, calibration, segment performance, latency, error budgets) and operating models in production
• Strong data engineering fluency: building reliable datasets and features using SQL, Spark/Databricks, and warehouse technologies (e.g., Snowflake), with an understanding of event semantics, identity resolution, and data quality controls
• Demonstrated experience in pricing/monetization or growth optimization domains preferred
• Sense of humor is optional but appreciated
• Working knowledge of experiment design and causal measurement for monetization systems, including pitfalls such as selection bias, interference, and delayed outcomes; familiarity with uplift modeling, bandits, or constrained optimization is a strong plus