Sr. ML Ops Engineer

Job description

Job responsibilities

• Build and maintain the data pipeline infrastructure that consolidates internal infra, labeling tools, S3, and other data sources into a unified, queryable system
• Build tooling for dataset selection and curation that can programmatically target specific data (by environment, object type, etc.)
• Own ML data infra from robot to training run, accessible to the ML team without backend engineering help
• Build model evaluation and regression testing infrastructure -- real metrics, not vibes or "someone complained in prod"
• Automate the model retuning loop for standard tasks so ML engineers can be mostly hands-off on routine updates

Job requirements

• 2-3 years shipping real production ML infrastructure for big datasets, not just scripts
• Experience building distributed data pipelines that consolidate multiple sources
• Demonstrated understanding of data flow from raw collection, labeled training set, to trained models
• Experience building systems from scratch, or contributed heavily to a small-team infra build where the playbook didn't exist
• Ability to thrive in a startup environment with high ambiguity. You'll figure out what to build