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Remote Warehouse Scanner Jobs in California (NOW HIRING)

Sr. ML Ops Engineer

Mountain View, CA · On-site +1

$123K - $169K/yr

Today, nearly 100% of warehouses track their inventory manually using barcode scanners and climbing ... hybrid or remote role with periodic trips to HQ in Mountain View, CA. Must Haves * 2-3 years ...

Senior Software Engineer

Mountain View, CA · On-site +1

$170K - $230K/yr

Today, nearly 100% of warehouses track their inventory manually using barcode scanners and climbing ... Travel with the CEO to customer sites to learn from user feedback This role is hybrid or remote (US ...

Remote Warehouse Scanner information

What is the difference between Remote Warehouse Scanner vs Warehouse Associate?

AspectRemote Warehouse ScannerWarehouse Associate
CredentialsBasic certifications, inventory management skillsHigh school diploma or equivalent, physical fitness
Work EnvironmentPrimarily remote with occasional on-site visitsOn-site warehouse setting
Job DutiesRemote inventory tracking, data entry, scanningPicking, packing, stocking, physical movement of goods
Industry UsageLogistics, supply chain managementWarehousing, retail distribution

The Remote Warehouse Scanner typically handles inventory management remotely, focusing on data entry and scanning tasks, often with minimal physical activity. In contrast, a Warehouse Associate performs hands-on tasks within the warehouse, including stocking and order fulfillment. Both roles are essential in logistics but differ mainly in work environment and daily responsibilities.

What are the most commonly searched types of Warehouse Scanner jobs in California?

The most popular types of Warehouse Scanner jobs in California are:

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For Remote Warehouse Scanner jobs in California, the most frequently searched job titles are:

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What cities in California are hiring for Remote Warehouse Scanner jobs?

Cities in California with the most Remote Warehouse Scanner job openings:

Sr. ML Ops Engineer

Corvus Robotics

Mountain View, CA • On-site, Remote

$123K - $169K/yr

Full-time

Re-posted 26 days ago


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. 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