Staff Machine Learning Engineer (Pricing)

GoFundMe•San Francisco, CA

4d•Hybrid

About The Position

GoFundMe is seeking a Staff Machine Learning Engineer specializing in Pricing to design, develop, and deploy machine learning systems that optimize pricing and monetization strategies. This role involves end-to-end execution of production ML systems, from data processing and model training to online inference and rigorous experimentation. The position requires a strong understanding of ML operational excellence, collaboration with cross-functional teams, and mentoring junior engineers. The role is based in the San Francisco Bay Area with a requirement of 3 days per week in the office.

Requirements

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.
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).
Demonstrated experience in pricing/monetization or growth optimization domains preferred.
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.
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.
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.
Experience implementing ML monitoring for both technical and business metrics (drift, calibration, segment performance, latency, error budgets) and operating models in production.
Ability to break down ambiguous, high-impact problems, define crisp interfaces and success metrics, and deliver iteratively with strong stakeholder communication.
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.

Nice To Haves

Full-stack experience—e.g., integrating with web clients and experimentation frameworks—is a plus.
Advanced degree (Master’s or Ph.D.) in Computer Science, Statistics, Data Science, or a related technical field is preferred.
Sense of humor is optional but appreciated.

Responsibilities

Own end-to-end ML systems for pricing optimization, from problem framing and metric definition to model development, launch, and iteration in production.
Design and implement backend model pipelines including feature engineering, training, and evaluation.
Build low-latency real-time inferencing services, including API design, caching strategies, model packaging, and deployment on Kubernetes.
Collaborate with teams to develop instrumentation and event pipelines to capture user and campaign activity required for training and evaluation, ensuring schema quality, lineage, and privacy-by-design.
Apply causal and experimental methodologies to measure impact and avoid biased optimization, including online A/B testing design, guardrail metrics, sequential testing considerations, and counterfactual/causal approaches.
Develop optimization approaches appropriate for pricing-like problems, such as uplift modeling, bandits, constrained optimization, calibration, and multi-objective tradeoffs.
Establish ML operational excellence by implementing model observability, automated retraining triggers, rollback strategies, and incident response playbooks for pricing systems.
Partner cross-functionally with Product, Engineering, Design, and Legal/Privacy stakeholders to translate business goals into measurable technical deliverables and ship safely.
Mentor and set technical direction for other engineers and scientists through design reviews, architecture decisions, and shared best practices for production ML in monetization.
Employ a diverse set of tools and platforms, including Python, AWS, Databricks, Docker, Kubernetes, FastAPI, Terraform, Snowflake, and GitHub, to develop, deploy, and maintain scalable and robust machine learning systems.