Software Engineer, Machine Learning Infrastructure

Design, build, and maintain tools to track the full ML model development lifecycle, including feature stores, experiment tracking, and model registries.
Implement robust deployment and evaluation workflows to ensure efficient, scalable model delivery.
Develop issue detection and alerting for critical ML services such as training jobs, data pipelines, and deployments to maximize uptime.
Create and maintain observability dashboards to monitor model performance, data quality, and system metrics.
Promote robust, reproducible, and debuggable ML experimentation through best practices and tooling.
Collaborate with cross-functional teams (perception, behavior, mapping, simulation) to identify infrastructure needs and integrate solutions across end-to-end ML workflows, balancing innovation with practicality.