Next-generation recycling technology. Backed by NEA.
About the role
Own our image and video dataset lifecycle: taxonomy, curation, collection workflows, and quality, so our models train on clean, well-structured, error-free data. Design and maintain the data pipelines that move, transform, version, and sync our datasets (e.g. DVC, annotation platforms, cloud storage).
What they're looking for
- 2+ years of Machine Learning experience
- 2+ years building software applications or internal tools, with strong expertise in Python
- 2+ years of experience working with image or video datasets
- Hands-on experience designing and maintaining data pipelines (ETL/ELT, workflow orchestration, data versioning)
- Strong SQL and experience working with structured data
- English fluency, as you'll be working with a US-based team (B2 or higher)
More about this role
Hey, we're Glacier! A Series A startup based in San Francisco tackling one of the world's most pressing problems: trash. Did you know that in the US, we send over half of our recyclables to the landfill? We're working to fix that. In doing so, we'll also be reducing carbon emissions, energy consumption, and depletion of natural resources.
Glacier builds custom sorting robots designed to sort apart recyclables as well as AI-powered business analytics that enable recyclers to superpower their plants and improve our society's circularity.
From major CPG companies like Colgate and Amazon to municipal recycling facilities, our clients trust us to turn recycling data into actionable insights. Our technology has been recognized as one of TIME's Best Inventions and featured in a TIME documentary , TechCrunch , Fortune , and CBS .
We're looking for a talented computer vision data engineer to own the data layer that powers our computer vision models. Great models start with great data, and you'll build the datasets, pipelines, and internal tools that let our ML team move fast and train on high-quality, well-curated data. This is a strong fit for someone who loves building software and...
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