ML Ops Production Lead

GoFundMe; San Francisco, Northern (CA, KY); Hybrid; USD 219,000 - 329,000; Full time

Benefits offered by this job

Equity

Healthcare

Dental

Vision

Life insurance

401(k)

Job summary

GoFundMe is hiring a Manager, Machine Learning Engineering to lead the ML/AI operations team responsible for reliable, scalable production systems across training pipelines, feature stores, and model serving.

You will mentor engineers, drive architecture decisions, and partner with data science to streamline model deployment and monitoring, ensuring safe and cost-effective AI at scale.

Qualifications

• 7+ years building production ML systems with backend services.
• 1-3+ years directly managing engineers in ML/AI ops or related.
• Strong Python and ML libraries (PyTorch, TensorFlow, scikit-learn).
• Experience with real-time model serving, containerization, scalable inference.
• Fluent data engineering with SQL, Spark/Databricks, data quality controls.
• Experience with ML monitoring (drift, calibration, latency, SLOs).
• Familiarity with generative AI/LLM infra and safety considerations.

Responsibilities

• Own reliability, scalability, and health of ML/AI production systems (training pipelines, model serving, monitoring).
• Lead, hire, and mentor ML/AI operations engineers and set technical direction.
• Align CI/CD for ML, model packaging/versioning/rollback strategies.
• Establish operational excellence: observability, automated retraining triggers, incident playbooks.
• Drive on-call processes, SLOs/SLAs, and postmortem practices for ML/AI systems.

Skills

ML leadership

People management

Python & ML libraries

Real-time model serving

Data engineering

ML monitoring

Generative AI familiarity

Architectural decisions

Stakeholder communication

Education

Advanced degree (MS/PhD) in CS/Statistics/DS

Tools

Docker

Kubernetes

Terraform

Snowflake

Databricks

GitHub

Job description

GoFundMe is hiring a Manager, Machine Learning Engineering to lead the ML/AI operations team responsible for reliable, scalable production systems across training pipelines, feature stores, and model serving.

You will mentor engineers, drive architecture decisions, and partner with data science to streamline model deployment and monitoring, ensuring safe and cost-effective AI at scale.