corvus-robotics - Sr. ML Ops Engineer
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 • Experience setting up annotation tooling and workflows • Background in robotics autonomy and computer vision • Experience integrating with tools like Kubeflow, SLURM, or similar for scalable training workflows
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 • This is a hybrid or remote role with periodic trips to HQ in Mountain View, CA.
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