SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
GoFundMe is seeking a Staff Machine Learning Engineer to design, develop, and deploy ML systems powering pricing and monetization across the platform. You will own end-to-end ML pipelines for donation yield optimization, recurring donor LTV, checkout personalization, and fundraising goal suggestions—from problem framing through production deployment and iteration.
Key responsibilities include: architecting backend ML pipelines with feature engineering, training, and evaluation; building low-latency real-time inference services on Kubernetes with sub-100ms latency targets; collaborating on event instrumentation and data pipelines for training data; applying causal and experimental methodologies (A/B testing, uplift modeling, bandits, constrained optimization) to measure impact and avoid bias; establishing ML operational excellence through monitoring, drift detection, automated retraining, and incident response; and mentoring engineers on production ML best practices.
You will partner cross-functionally with Product, Engineering, Design, and Legal/Privacy teams to translate business goals into technical deliverables. The tech stack includes Python, AWS, Databricks, Docker, Kubernetes, FastAPI, Terraform, Snowflake, and GitHub. Full-stack experience integrating with web clients and experimentation frameworks is a plus.
Required: 7+ years building and shipping production ML systems with demonstrated ownership of backend services in high-availability environments. Strong Python proficiency, ML frameworks (PyTorch, TensorFlow, Scikit-learn), and software engineering fundamentals. Experience designing and deploying real-time model serving, strong data engineering fluency (SQL, Spark, Snowflake), working knowledge of experiment design and causal measurement for monetization, and ML monitoring for technical and business metrics. Ability to break down ambiguous problems, define success metrics, and communicate with stakeholders. Strong leadership and mentoring skills.
Preferred: Master's or Ph.D. in Computer Science, Statistics, Data Science, or related field; demonstrated experience in pricing/monetization or growth optimization domains; familiarity with uplift modeling, bandits, or constrained optimization.
Role requires 3x/week in-office presence in San Francisco Bay Area.
