Sr. ML Ops Engineer
Confirmed on the employer's own hiring board on Sep 27, 2026. First seen by Alion on Jun 1, 2026. Corvus Robotics scores C on the Alion truth index.
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. Our fully autonomous Corvus One™ 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