Manager, Machine Learning Engineering

GoFundMe•San Francisco, CA

10d•$219,000 - $329,000•Hybrid

About The Position

GoFundMe is seeking a Manager, Machine Learning Engineering (ML and AI Operations) to lead the team responsible for the infrastructure, pipelines, and operational rigor of GoFundMe's machine learning and AI systems. This role requires strong technical judgment across the ML lifecycle, a deep understanding of operating AI applications safely at scale, and proven leadership abilities to build and manage a high-performance team. The position 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.
1-3+ years of experience directly managing engineers, ideally in an MLOps, ML platform, or infrastructure context, with a track record of hiring and developing strong teams.
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).
Experience designing and operating real-time model serving at scale, 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.
Proven experience implementing ML monitoring for both technical and business metrics and running models reliably in production.
Ability to break down ambiguous, high-impact problems, define crisp interfaces and success metrics, and deliver iteratively while managing stakeholder expectations.
Strong leadership and mentoring skills and a proven ability to raise the bar on architecture, engineering quality, and operational rigor for production ML/AI systems.

Nice To Haves

Familiarity with generative AI/LLM infrastructure and operational considerations (latency, cost, safety guardrails) is a strong 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 the reliability, scalability, and operational health of ML/AI production systems, including training pipelines, feature stores, model serving, and monitoring/observability infrastructure.
Lead, hire, and grow a team of ML/AI operations engineers, setting technical direction through design reviews, architecture decisions, and shared best practices.
Partner with data science and ML engineering teams to streamline the path from model development to production deployment, including CI/CD for ML, model packaging, versioning, and rollback strategies.
Establish ML operational excellence org-wide by driving standards for model observability, automated retraining triggers, and incident response playbooks.
Build and mature on-call processes, SLOs/SLAs, and postmortem practices for ML/AI systems.
Drive operational strategy for GoFundMe's generative AI systems alongside traditional ML, balancing innovation velocity with safety, compliance, cost, and reliability.
Collaborate cross-functionally with Product, Engineering, Design, and Legal/Privacy stakeholders to translate business goals into team priorities and measurable operational outcomes.
Manage vendor and platform relationships and make build-vs-buy calls.
Report on team health, system reliability metrics, and operational risk to senior engineering leadership.
Employ a diverse set of tools and platforms, including Python, AWS, Databricks, Docker, Kubernetes, Terraform, Snowflake, and GitHub, to guide your team in developing, deploying, and maintaining scalable and robust machine learning systems.