Sr. ML Ops Engineer
Corvus Robotics3 months ago
Responsibilities
- Build and maintain data pipeline infrastructure that unifies internal infrastructure, labeling tools, S3, and other data sources into a queryable system.
- Build programmatic tooling for dataset selection and curation by environment, object type, and other criteria.
- Own ML data infrastructure from robot data collection through training runs for direct use by the ML team.
- Build model evaluation and regression testing infrastructure with measurable metrics.
- Automate routine model retuning workflows so ML engineers can operate them with minimal manual involvement.
Requirements
- 2–3 years of experience shipping production ML infrastructure for large datasets.
- Experience building distributed data pipelines that consolidate multiple data sources.
- Understanding of the data flow from raw data collection through labeled training sets to trained models.
- Experience building systems from scratch or making substantial contributions to infrastructure in a small team without an established playbook.
- Ability to work effectively in a high-ambiguity startup environment.
- Experience setting up annotation tooling and workflows is preferred.
- Background in robotics autonomy and computer vision is preferred.
- Experience integrating scalable training workflows with Kubeflow, SLURM, or similar tools is preferred.
Benefits
- Hybrid or remote work arrangement with periodic trips to headquarters in Mountain View, California.
Categories
Data EngineeringML Engineering
About Corvus Robotics
Corvus Robotics takes the grunt work out of inventory tracking with fully autonomous drones that scan, count, and track pallets of inventory. Ditch the clipboards, boost your inventory accuracy to 99.% and free up your team for more valuable tasks. Real-time visibility, fewer stock surprises, and way less time, energy, and money spent counting boxes. Smarter warehouses start here.