Staff Machine Learning Engineer

About the role

GoFundMe is on the lookout for a Staff Machine Learning Engineer who will be instrumental in developing and managing machine learning systems that enhance pricing and monetization strategies across our platform. In this role, you will focus on creating personalized donation experiences, optimizing yield for both one-time and recurring contributions, modeling donor lifetime value, and recommending fundraising goals. This position requires comprehensive ownership of the machine learning lifecycle, from data collection and processing to production deployment, with a strong focus on experimentation and system monitoring.

Key facts

What you'll do

• Spearhead the development of machine learning systems aimed at optimizing pricing, which includes defining problems, selecting metrics (such as conversion rates, retention, donation yield, and lifetime value), creating models, deploying them, and refining them over time.
• Design and implement backend data pipelines for feature engineering, model training, and performance assessment.
• Develop real-time inference services with minimal latency, addressing API design, caching strategies, model packaging, and deployment using Kubernetes.
• Collaborate with cross-functional teams to establish instrumentation and event pipelines that capture user and campaign interactions (like impressions, clicks, donations, tips, and recurring enrollments) while ensuring schema integrity and compliance with privacy regulations.
• Utilize causal inference and experimental methodologies such as A/B testing, guardrail metrics, sequential testing, and counterfactual analysis to accurately assess the impact of changes.
• Employ specialized techniques for pricing challenges, including uplift modeling, bandit algorithms, constrained optimization, and balancing competing objectives like yield and donor trust.
• Set operational benchmarks through model observability, which includes monitoring latency, errors, drift, calibration, and shifts in business metrics, alongside automated retraining and incident response documentation.
• Work closely with Product, Engineering, Design, and Legal/Privacy teams to translate business goals into actionable technical solutions.
• Mentor and guide fellow engineers and data scientists through design reviews, architectural decisions, and the establishment of best practices in machine learning.

Requirements

• A minimum of 7 years of experience in building and deploying production-level machine learning systems, with a focus on backend services and pipelines in high-availability environments.
• Proficient in Python and experienced with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Strong foundation in software engineering principles, including testing, code reviews, CI/CD practices, API design, performance optimization, and reliability.
• Proven experience in deploying real-time model serving solutions with latency under 100 milliseconds to low-hundreds of milliseconds, including containerization, scalable inference, feature retrieval, and safe rollout strategies (like canaries and shadowing).
• Expertise in data engineering, capable of constructing reliable datasets and features using SQL, Spark/Databricks, and data warehousing tools like Snowflake, with a solid understanding of event semantics, identity resolution, and data quality.
• Familiarity with experimental design and causal measurement techniques for monetization, including an understanding of selection bias, interference, and delayed outcomes.
• Experience in monitoring machine learning systems, focusing on both technical and business metrics (such as drift, calibration, segment performance, latency, and error budgets).
• Ability to break down complex, high-impact problems, establish clear interfaces and success criteria, and deliver results iteratively while maintaining effective communication.
• Demonstrated leadership and mentorship capabilities, with a history of elevating standards in architecture, engineering quality, and operational discipline.

Nice to have

• Background in pricing, monetization, or growth optimization sectors.
• Familiarity with uplift modeling, bandit algorithms, or constrained optimization techniques.
• Full-stack experience, particularly in integrating with web clients and experimentation frameworks.
• An advanced degree (Master's or Ph.D.) in Computer Science, Statistics, Data Science, or a related technical discipline.

Skills & tools

• Proficient in Python, PyTorch, TensorFlow, Scikit-learn
• Familiar with AWS, Databricks, Snowflake
• Experienced with Docker, Kubernetes, FastAPI, Terraform
• Competent in SQL, Spark
• Knowledgeable in GitHub and CI/CD pipelines

Practical notes

• Compensation package includes equity, healthcare benefits, dental and vision coverage, life insurance, and a 401(k) plan.
• Salary may be adjusted based on factors such as location, skills, experience, and educational background.
• Benefits offered include support for hybrid work arrangements, family planning assistance, generous parental leave, flexible time off, and mental health resources.
• Requests for accommodations can be directed to accommodationrequests@gofundme.com.
• Since its inception in 2010, the GoFundMe community has successfully raised over $40 billion.