Sr. ML Ops Engineer

Job description

## **About Corvus** Every physical good spends time in a warehouse, and every warehouse tracks their inventory. Today, nearly 100% of warehouses track their inventory manually using barcode scanners and climbing forklifts. We're [Corvus Robotics](https://www.corvus-robotics.com/). Our fully autonomous [Corvus One™](https://blog.corvus-robotics.com/corvus-one-launch-and-series-a-funding) drones use computer vision & robotics to automatically track inventory, improving worker safety and increasing labor efficiency. We believe that data-driven, safe inventory management will optimize the global physical economy and improve economic prosperity for humanity. ## **About the Role** With a growing fleet of autonomous drones and an expanding customer base, we're now ready to multiply ML iteration speed and unblock more advanced ML product delivery. We're hiring a **systems-oriented Senior Software Engineer** to build the data infrastructure, training pipelines, and internal tooling that our ML team needs to move faster. Specifically in this role you will: - 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. ## **Must Haves** - 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 ## **Nice to Haves** - 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