layiq
/la·yiq/adjective
worthy; deserving; fitting; suitable.
A role, opportunity, or path that merits attention, time, and pursuit.
Loading LAYIQ…
Job opportunity
Sr. ML Ops Engineer
Corvus Robotics
United States; Mountain View, California, United States
Source: Corvus Robotics careers · View original posting
Employer job description
From Corvus Robotics's posting. “We” and “our” refer to the employer.
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